JP2023513540A - Information Theory Genomics Enabling Hyperscalability - Google Patents

Abstract

The present disclosure relates to an information theory-based security platform and corresponding digital genome constructs that exhibit controlled entropy while maintaining genome integrity by computationally complex functions and processes. It is capable of undergoing digital alteration and reconstruction. These structures enable the formation of comprehensively secure, hyperscalable digital ecosystems, enclaves, and/or digital cohorts with identity of mutual interest, and genomic networks interoperable with modern application and network stacks. Realize application-specific security architecture based on topology.

Description

This application claims priority to U.S. Provisional Patent Application No. 62/970,304, entitled "Genome-Based Security Platform," filed February 5, 2020, the contents of which are incorporated by reference in its entirety. is incorporated herein.

The present disclosure presents an information theory-enhanced security platform and corresponding digital genome structure that exhibits controlled entropy but undergoes digital modification and reconstruction by computationally complex functions and processes without loss of genome integrity. It is about. These structures enable the formation of comprehensively secure, hyperscalable digital ecosystems, enclaves, and/or digital cohorts with identity of mutual interest, and genomic networks interoperable with modern application and network stacks. Realize application-specific security architecture based on topology.

The Noble as the ARPAnet's neighboring nodes break through the boundaries and become a World Wide Web composed of virtual digital ecosystems underpinned by an interoperable Digital Monoculture. The requirement to distinguish between unfair and nefarious has become more urgent. By the time the significant impacts facing this new machine-connected world become apparent, first responder technologies (firewalls, analytics, forensics, PKI, proxies, surveillance, etc.) will already be patrolling network perimeters. and relegated to rapid recovery services.

It is widely accepted among experts that cryptography is the only provable security solution in the connected world of machines. Research into quantum cryptography, homomorphic cryptography, and obfuscated cryptography has attracted enormous investment, but has not had the impact that was expected. Nevertheless, hyperscalability, the most essential yet essential complication of all cryptography disciplines, remains untouched. Efforts to update PKI to a post-quantum state have been extensive, despite its sustained linear scalability.

Applicants have developed a Cyphergenics (CG) technology that comprehensively solves the previously difficult hyperscalability dilemma, and disclosed the description herein. As we will see, Ciphergenics enables unlimited deployment of cryptographic-based digital ecosystem security with minimal overhead and bandwidth while maintaining full interoperability. Importantly, hyperscalability directly facilitates functional homomorphic encryption and functional indistinguishability obfuscation.

Attacks on cyber infrastructure and attacks on privacy both utilize the same digital ecosystem (the world of machines that are connected) and digital monoculture (the world in which everything is interoperable), so they are artificially two-dimensional. The split has always been, and always will be, irrelevant. ) security solutions admirably adapt and extend traditional security technologies and practices, yet remain post-mortem experts and poorly thwarted pedestrian Krugs. These technologies are notoriously limited in their ability to thwart sabotage, espionage, embezzlement, piracy, and invasion of privacy (even covert surveillance). An ever-growing digital attack surface, with weaponized malware, powerful new breeds of processors, and ultimately quantum-computer-based cryptanalysis and destructive algorithms powered by artificial intelligence, portends new levels of catastrophe. Let

Authentic engagement is driving the previously unimaginable virtualization of products, services and knowledge, redefining efficiency and efficacy. On the other hand, even within the same network-centric campus, unauthorized engagement can result in catastrophic cyberattacks (sabotage, espionage, extortion, piracy, etc.) and widespread attacks on privacy (covert mass surveillance, profiling, assimilation, etc.). etc.). The common machine language and inherent hyperscalability essential to network-centric missions make the noble and the villainous virtually indistinguishable.

Applicant believes that the most important network-centric capabilities are digital ecosystems consisting of various digital cohorts (e.g., networks, grids, clouds, systems, devices, appliances, sensors, IoT, applications, files, data, etc.). I propose that it is not even that interoperable digital monoculture. It is precisely their shared common machine language and hyperscalability to scale ubiquitous on-demand engagement (e.g., connectivity, communication, collaboration, coordination, etc.) that will enable hundreds of billions of unmanned security instances, billions of cohorts, millions of control points, and hundreds of billions of security instances per day.

Rebuilding digital ecosystems and digital monocultures remains unrealistic, endlessly disruptive, and financially imprudent. Technologies that can modify common machine languages based on computationally quantum-proven cryptography provide highly effective security while ruling out the hyperscalability that is essential for interoperability of the digital monoculture. deaf.

Ciphergenics (CG), an entirely new technology based on computationally complex genome structures, liberates modern cryptography from its esoteric yet powerful computationally complex foundations. In embodiments, CG enables virtual unconstrainedness by generating an information-theoretically constructed genome structure, exhibiting computational complexity. CG digital rendering is not a complex structure that can be directly controlled like biochemistry. Importantly, CG digital rendering greatly expands the range of its inherent differences and correlations while maintaining its biochemical unrestricted character. CG retains the ability to strategically control genomic structures based on different digital DNAs without compromising computational integrity. FIG. 1 is a diagram illustrating attributes of a ciphergenics-based digital ecosystem in relation to relevant attributes of organic and contemporary digital ecosystems, according to some embodiments of the present disclosure.

In embodiments, with unique genomic and cryptographic properties fused by information theory principles, ciphergenics-based technologies offer high-performance hyperscalability that exhibits unlimited differences (affiliation) and correlations (authentication). can be achieved. These properties give rise to virtual affiliation, virtual authentication, virtual agility, virtual organic engagement, and virtual trust effective domains, and are applied as powerful attributes far beyond security.

In embodiments, ciphergenics enables hyper-scalability of specific digital ecosystems, enclaves, and cohorts, and the mutual identity of interests (digital DNA-related and regulated ). These domain-resident ecosystems, enclaves, and cohorts have unique, non-repetitive, and computationally resilient attributes that reflect their mutual interest identity called Virtual Binary Language Scripts (VBLS). Engage based on hyper-scalable digital data objects and digital coder objects that demonstrate and share malicious intent bluntly.

While ciphergenics (CG) technology can be supported by a wide range of digital capability platforms and component configurations, some embodiments of the present disclosure are designed to help the ordering of important computationally complex genome construction and digital DNA regulatory functions and processes. Constructed to ensure certain performance. In embodiments, the Ciphergenics Ecosystem Security Platform (CG-ESP) may be composed of modules that control specific computational and genomic construction and digital DNA regulatory functions. This adaptability is important given that in embodiments, ciphergenic functionality may be rendered with cryptography, without any cryptography, or in combination.

In embodiments, Ciphergenics supports applications beyond network-centric interests so that its module-based rendering can serve multiple purposes. For example, they allow us to re-imagine, incrementally improve or modify the building and regulatory processes and functions that individual genomic information theory has enabled without compromising hyperscalability, and they are useful for ciphergenic applications. Allows computation and functional innovation between readiness attributes.

Security applications, almost without exception, need to tolerate network configurations. For example, the expansion of IP-IV address depletion due to NAT evasion for IP-SEC security. Ciphergenics VBLS attributes enable powerful new security-application-centric genomic network topologies to run concurrently, interoperably, and on-demand on existing network configurations. Ciphergenics enables many security-centric genomic network topologies such as Directed Architectures, Spontaneous Architectures, Ephemeral Architectures and Interledger Architectures . FIG. 2 illustrates an example of this adaptability, a ciphergenics-enabled security stack that can be applied concurrently at various layers of commonly known application and network stacks, according to some embodiments of the present disclosure. and examples of ciphergenics-enhanced genomic architectures of digital ecosystems that can result from such applications.

In embodiments, the extent of genome construction enabled by the information theory of ciphergenics allows digital cohorts to be spawned as offspring of specific corporations and/or enclaves prior to their own conception. . In embodiments, the cyphergenics-enabled digital ecosystem can be rendered gnomatically flat or hierarchical, with or without order, eg, orientation with respect to time. In embodiments, the ciphergenic cohort can act as a carrier of the Cambrian genome.

The differences between cyphergenics, the implementation and practice of which are described in the details of this patent, and the search for postmodern (quantum proof) cryptography are summarized below, an example of which can be observed in FIG. The challenges of network-centric security are expected to become more and more serious in the future, but countering cryptanalysis using quantum computers is nothing but maintaining the status quo.

In embodiments, ciphergenics-based technologies can replace underlying approaches as opposed to developing variants of existing technologies and their inherent limitations.

The present disclosure relates to different implementations of ciphergenics-based technologies and security platforms that are applied in myriad digital ecosystems with a wide range of mutual identities and topologies of differing interest. In embodiments of the present disclosure, instances of ciphergenics-based security platforms are configured for different types of digital ecosystems with different architectures to optimize different aspects of each ecosystem they serve. can be

According to some embodiments of the present disclosure, an ecosystem security platform is disclosed. The ecosystem security platform is run by VDAX's processing system. The ecosystem security platform includes a root DNA module, a link module, a sequence (sequence) mapping module, and a binary conversion module. The root DNA module is configured to manage digitally generated genomic datasets assigned to VDAX. Genomic datasets are specific to VDAX and include genome eligibility objects, genome correlation objects, and genome differentiation objects. A root DNA module is configured to modify the genomic dataset using one or more computationally complex functions. A link module is configured to receive the link from the second VDAX. The link contains encoded genomic control instructions. A link module is configured to decode the link based on the genome eligibility object and the modified genome correlation object to obtain decoded genome regulation instructions. The modified genome correlation object is modified from the genome correlation object by the root DNA module. A sequence mapping module is configured to obtain a sequence from the digital object to be provided to the second VDAX. A sequence is extracted from the first portion of the digital object. A sequence mapping module is configured to map sequences to modified genomic differentiation objects to obtain genomic engagement factors. The modified genome differentiation object is modified from the genome differentiation object by the root DNA module based on the decoded genome regulation instructions. A binary conversion module is configured to encode a second portion of the digital object based on the genomic engagement factor to obtain an encoded digital object. A binary conversion module is configured to generate a virtual binary language script (VBLS) object including the encoded digital object and the first portion of the digital object. The VBLS object is provided to the second VDAX as part of a series of VBLS objects.

In some embodiments, the digital objects may be part of a series of digital objects, each encoded into a series of respective VBLS objects. Each digital object may be encoded with a different respective genomic engagement factor. In some embodiments, the sequence mapping module may obtain each sequence from each digital object and map each sequence to the modified genome differentiation object to encode each digital object. Each genome engagement factor that can be used may be obtained. In some embodiments, the series of VBLS objects may be non-recurrent language specific to a VDAX and a second VDAX. In some embodiments, the sequence may be a public sequence defined in the first portion of the digital object according to known protocols and formats. In some embodiments, the sequence may be a private sequence that may be defined in the first portion of the digital object according to proprietary protocols and formats that may not be publicly available.

In some embodiments, the binary conversion module can include a disambiguation module that encodes the second portion of the digital object based on the genomic engagement. An encryption module may be included that encrypts the second portion of the digital object based on the genomic engagement factor by using an XOR operation to determine the connection binding between the second portion and the genomic engagement factor. In some embodiments, the binary conversion module converts the second portion of the digital object based on the genomic engagement factor by encrypting the second portion of the digital object using the encryption function and the genomic engagement factor. It may also include an encryption module for encryption.

In some embodiments, the link module may decode the link from the second VDAX as part of the link exchange process with the second VDAX. The link exchange process may be a one-time process. In some embodiments, the link exchange process may include authenticating the ecosystem member associated with the second VDAX based on the VDAX's genomic eligibility object. In some embodiments, the link module may be further configured to produce a second link that includes a second encoded genome regulatory instruction. A second link may be provided to the second VDAX as part of the link exchange process. In some embodiments, the link exchange process may be a bisymmetric process such that the second link may be generated independently of the link received from the second VDAX. In some embodiments, when producing the second link, the link module may be further configured to determine a second genome regulatory instruction. The link module may be further configured to encode the second genome regulatory instructions using the link genome engagement factor to obtain second encoded genome regulatory instructions. A link genome engagement factor may be determined by the sequence mapping module based on the link mapping sequence and a second modified genome correlation object that may be modified from the genome correlation object by the root DNA module. The link module may be further configured to generate a genome engagement cargo that includes the link mapping sequence, the second encoded genomic adjustment instructions, and the encoded link decoding information. The link mapping sequence may not be obfuscated in the genome engagement cargo and the second link may contain the genome engagement cargo. The link module may be further configured to provide a second link to the second VDAX. The second VDAX may decode the second encoded genomic adjustment instructions based on the link mapping sequence, the encoded link decoding information, and the second genomic data set assigned to the second VDAX. . In some embodiments, the link mapping sequence may remain unencoded in the genome engagement cargo. In some embodiments, the binary conversion module may be further configured to receive the second VBLS object from the second VDAX. A second VBLS object may include a second encoded digital object and unencoded metadata. The binary transform module may be further configured to decode the second digital object based on the second genome engagement coefficient to obtain an unencoded second digital object. a second genomic engagement factor based on the second sequence and a second modified genomic differentiation object that can be derived from the genomic differentiation object of the genomic data set by the root DNA module based on the second genomic regulatory instructions; It may be determined by a sequence mapping module.

In some embodiments, a link module may be configured to perform a link exchange process over a set of interoperable digital communication media. In some embodiments, a link module may be configured to perform a link exchange process across a set of interoperable digital networks. In some embodiments, a link module may be configured to perform link exchange processing across a set of interoperable digital devices. In some embodiments, the link module may be configured to perform link exchanges that may be performed asynchronously with respect to the second VDAX. In some embodiments, the link module may be configured to perform link exchange processing in a symmetrical manner such that a VDAX may not provide a second link to a second VDAX. In some embodiments, the linking module may be further configured to identify eligibility correlations for a second VDAX based on the VDAX's genomic eligibility correlation object. In some embodiments, the link module may be further configured to ascertain link exchange correlations for the second VDAX based on the links received from the second VDAX and the VDAX's genomic correlation objects. In some embodiments, the link module may be configured to perform secure exchange of link information using a set of computationally complex functions. The set of computationally complex functions may be one of cryptographic-based functions, cryptographic-free functions, or hybrid functions that may include at least one cryptographic-based function and at least one cryptographic-free function. In some embodiments, the link module may be configured to securely exchange link information with the second VDAX and perform a series of operations to enable bisymmetric engagement. The exchange of link information may exhibit the same level of entropy as asymmetric engagement. In some embodiments, the link module may include a static link module that may be configured to generate and decode static links. The static link module may generate static links according to the rules and processes defined by VDAX, the premier ecosystem within the digital ecosystem. In some embodiments, the link module may include a dynamic link module that may be configured to generate and decode dynamic links. The link received from the second VDAX is a dynamic link that further includes an instruction set that, when executed by the VDAX, can overwrite the configuration of each of at least one of the root DNA module, the sequence mapping module, or the binary conversion module. may The modified configuration may only be performed when generating a VBLS that can be provided to the second VDAX.

In some embodiments, the sequence mapping module may be configured to select a published sequence from each first portion of each digital object that may be formatted according to a publicly available protocol. In some embodiments, in mapping a sequence to a modified genome differentiation object, the sequence mapping module may do the following. It is configured to process the sequence to derive intermediate values. The sequence mapping module may be configured to generate genomic engagement factors based on median values and modified genomic differentiation targets using a set of information-theoretically-enhanced computational complexity functions. In an exemplary embodiment, the set of information-theoretically facilitated computational complexity functions includes cryptographic-based functions, cryptographic-free functions, or at least one cryptographic-based function and at least one cryptographic-free function. It can be one of the hybrid functions. In an exemplary embodiment, the genomic engagement factor may be a binary vector indicating a particular entropy. The specific entropy of the genome engagement factor may be greater than or equal to the intrinsic entropy of the sequence. In an exemplary embodiment, the sequence mapping module may be configured to select a sequence from each first portion of each digital object that may be formatted according to its own protocol.

In some embodiments, the root DNA module may include a CNA module that can formulate and build genome-qualified objects. CNA modules may be configured to employ information-theoretically-enhanced genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems. In some embodiments, the link module may use the genome eligibility object to confirm genome engagement consistency with the second VDAX. In some embodiments, a genome-qualifying object may be a CNA object that exhibits a certain entropy. CNA objects may enable genomic processing based on differences and correlations. A CNA object may be an N-dimensional binary vector indicating a certain entropy. A particular entropy of a CNA object may be configurable by a community owner. In some embodiments, the CNA module may be configured to generate a respective set of CNA objects based on the PNA objects of VDAX. Each set of CNA objects may be respectively assigned to a set of respective descendant VDAXs, and each CNA object may exhibit a particular entropy equal to the entropy of the CNA object of the VDAX. In some embodiments, the CNA module uses a set of information-theoretically-enhanced computational complexity functions to determine eligibility with respect to other VDAXes based on CNA objects and engagement information provided by other VDAXes. It may be configured to process the correlation. The set of information-theoretically facilitated computational complexity functions is one of cryptographic-based functions, cryptographic-free functions, or hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. can be In some embodiments, the CNA module may be configured to establish specific relationships between other VDAXes within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's CNA objects.

In some embodiments, the root DNA module may include a PNA module that formulates and builds genome qualification objects. PNA modules may be configured to employ information-theoretically-enhanced genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems. In some embodiments, the link module may use the genome eligibility object to confirm genome engagement eligibility with the second VDAX. A genome-qualifying object may be a PNA object that exhibits a certain entropy. PNA objects may enable genomic processing based on differences and correlations. In some embodiments, a PNA object may include a first N-dimensional binary vector and a second N-dimensional binary vector that indicate a particular entropy. The first N-dimensional vector may be composed of M randomly selected binary primitive polynomials of degree T such that M×T equals N, and the second N-dimensional vector may be composed of the first is determined based on the N-dimensional binary vector of In some embodiments, the specific entropy of PNA objects may be configurable by community owners. In some embodiments, the PNA module may be configured to generate a respective set of PNA objects based on the VDAX PNA objects. Each set of PNA objects may be respectively assigned to a respective set of descendant VDAXs, and each PNA object may exhibit a particular entropy equal to the entropy of the PNA objects of the VDAX. In some embodiments, the PNA module uses a set of computational complexity functions based on information theory to process eligibility correlations for other VDAXs based on PNA object and engagement information provided by other VDAXes. may be configured to The set of computational complexity functions based on information theory is one of cryptographic-based functions, non-cryptographic functions, and hybrid functions comprising at least one cryptographic-based function and at least one non-cryptographic function. good. In some embodiments, the PNA module may be configured to establish specific relationships between other VDAXes within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's PNA objects.

In some embodiments, the root DNA module may include an LNA module that formulates and builds genomic correlation objects. LNA modules may be configured to employ information-theoretically-enhanced genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems. In some embodiments, the link module may use the genome eligibility object to verify the link exchange correlation with the second link-based VDAX. In some embodiments, a genome-qualifying object may be an LNA object that exhibits a certain entropy. LNA objects may enable differential and correlation-based genome processing that can be performed during link exchange. The LNA object may be sufficiently correlated with the second LNA object of the second VDAX. In some embodiments, the specific entropy of LNA objects may be configurable by the community owner. In some embodiments, an LNA object may be an N-dimensional binary vector that exhibits a certain entropy. In some embodiments, the LNA module uses a set of computational complexity functions based on information theory to generate other may be configured to look up link eligibility correlations for VDAX of In some embodiments, the set of information theory-based computational complexity functions includes cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. can be one of In some embodiments, the LNA module may be configured to establish specific relationships between other VDAXes within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's LNA objects. In some embodiments, the LNA module may be configured to generate a respective set of LNA objects based on the VDAX LNA objects. Each set of LNA objects may be assigned to a respective set of descendant VDAX. Each LNA object may exhibit a certain entropy equal to the entropy of the VDAX LNA object. In some embodiments, the LNA module may be configured to modify genomic correlation objects based on a specific set of instructions using a set of information-theoretically-enhanced computationally complex functions. . The set of computationally complex functions facilitated by information theory is one of cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. can be one. In some embodiments, the LNA module may modify the genomic correlation object based on a set of instructions received from the ancestral VDAX. Modification of genomic correlation objects may be used to establish future engagements regarding the digital ecosystem to which VDAX belongs, while previously established engagements may remain unaffected. In some embodiments, the LNA module may modify the genome correlation object based on a series of instructions received from the second VDAX in the link to obtain the modified genome correlation object. The modified genome correlation object may be used to determine link genome engagement factors that can be used to decode the encoded GRI.

In some embodiments, the root DNA module may include an XNA module that performs genomic processes involving genome correlation objects, including at least one of generating new genome correlation objects and modifying genome correlation objects in VDAX. good. In some embodiments, the XNA module employs information-theoretically-enhanced genomic processes to identify itself to a second VDAX according to genomic regulatory instructions deciphered from links obtained from the second VDAX. The method may be configured to modify the VDAX genome derivative object. In some embodiments, a genome-qualifying object may be an XNA object that exhibits a certain entropy. In some embodiments, the XNA object may be sufficiently correlated with the second XNA object of the second VDAX. In some embodiments, the specific entropy of an XNA object may be configurable by the community owner. In some embodiments, an XNA object may be an N-dimensional binary vector that exhibits a certain entropy. In some embodiments, XNA objects may be used to establish future differentiation from other VDAXs that possess sufficiently correlated XNA objects. In some embodiments, the XNA module may be configured to generate a respective set of XNA objects based on the VDAX XNA objects. Each set of XNA objects may be assigned to a respective set of descendant VDAX. Each XNA object may exhibit a certain entropy equal to the entropy of the VDAX XNA object. In some embodiments, the XNA module may be configured to modify XNA based on a particular set of instructions using a set of information-theoretically-enhanced computationally complex functions. In some embodiments, the XNA module receives a set of data from an ancestor VDAX so that the updated XNA can be used to establish future differentiation against other VDAXs within the digital ecosystem to which the VDAX belongs. XNA may be updated based on the instructions. In some embodiments, a second VDAX cannot establish future differentiation with a VDAX unless the second VDAX possesses sufficiently correlated and continuously updated XNA. There is In some embodiments, the set of information-theoretically-enhanced computationally complex functions includes cryptographic-based functions, cryptographic-free functions, and at least one cryptographic-based function and at least one cryptographic-free function. can be one of the hybrid functions containing

In some embodiments, the ecosystem security platform may further include an authentication module that charges secure genome-based engagement correlations with respect to the second VDAX according to an information-theoretically driven computational complexity function. The information-theoretically facilitated computationally complex function is one of a cryptographic-based function, a cryptographic-free function, and a hybrid function that can include at least one cryptographic-based function and at least one cryptographic-free function. can be In some embodiments, the ecosystem security platform may further include a master integrity controller that may include a genomic process controller, an authentication module, and an engagement instance module. A genomic process controller may have a master controller genomic dataset assigned to it.

In some embodiments, a genomic process controller may be configured to engage with one or more platform modules to authenticate and verify the integrity of the one or more platform modules. In some embodiments, the genomic process controller may verify and authenticate the integrity of one or more platform modules based on the master controller genomic dataset and a set of computationally complex functions. In some embodiments, the genomic process controller may verify and authenticate the integrity of one or more platform modules without determining any processes or functions performed by the one or more platform modules. good. In some embodiments, the genomic process controller uses a set of computational complexity functions to check and authenticate the integrity of any underlying operational processes and functions that connect one or more platform modules. It may be further configured. In some embodiments, the genomic process controller may be further configured to validate, disqualify, or initiate changes to one or more platform modules and underlying operational processes and functions.

In some embodiments, the authorization module is based on the genome controller genomic data and a set of information-theoretically driven computational complexity functions to confirm or deny the operational configuration of other VDAXs within the digital ecosystem. may be configured. In response to determining to deny the other VDAX's operational configuration, the authorization module may disqualify or initiate modification of the other VDAX's operational configuration.

In some embodiments, the engagement instance module is configured to track security instances within VDAX's digital ecosystem according to a set of one or more engagement tracking policies that define one or more definitions of security instances. may be In some embodiments, the engagement instance module determines the number of security instances within VDAX's digital ecosystem according to a set of one or more engagement account policies, each of which defines how security instances are counted. may be further configured as follows. In some embodiments, the Engagement Instances module defines one or more Engagement Reporting Policies that define how security instances are reported, to which VDAX to report security instances, and how often to report security instances, respectively. may be further configured to determine the reporting of the number of security instances of the digital ecosystem to another VDAX according to the set of .

In some embodiments, VDAX is a member of a digital ecosystem. In some of these embodiments, the digital ecosystem is a cloud service system. In some of these embodiments, the digital ecosystem is an enterprise information technology system. In some of these embodiments, the digital ecosystem is a computing device and descendant VDAXs correspond to hardware and digital components of the computing device, respectively. In some of these embodiments, the digital ecosystem is a classified computing infrastructure. In some embodiments the digital ecosystem is a traffic grid. In some embodiments, the digital ecosystem is a home network.

According to some embodiments of the present disclosure, a system for performing genome security-related controls of a digital ecosystem is disclosed. This system includes an ecosystem VDAX executed by a processing system associated with the owner of the digital ecosystem. An ecosystem VDAX consists of an ecosystem instance of the ecosystem security platform, and an ecosystem VDAX maintains an ancestral genome dataset corresponding to the digital ecosystem containing one or more different digitally generated ancestral genomic data objects. constructed, each ancestral genome data object exhibits its particular entropy. The ecosystem VAX is further configured to generate a plurality of respective progeny genome datasets based on the ancestral genome datasets, each respective progeny genome dataset comprising one or more digitally generated ancestral genome datasets. Contains one or more different progeny genomic data objects each derived from the data object and exhibiting a particular entropy for each of the progenitor genomic data objects from which it was derived. In these embodiments, the ecosystem VDAX, for each respective offspring genome data set, assigns the offspring genome data set to each offspring VDAX of the plurality of offspring VDAX, and the offspring VDAX has no further interactions from the ecosystem VDAX. Second, it is configured to establish unique non-recurrent engagements with other offspring VDAXs within the digital community based on the respective offspring genomic datasets assigned to the offspring VDAX. Ecosystem VDAXs can influence the ability of a given descendant VDAX to engage with other VDAXs in the digital ecosystem by selectively updating one or more of the descendant genomic datasets. It is further configured to control genome topology.

In some embodiments, the ancestral genomic datasets include ancestral genomic differentiation objects, and each ancestral genomic dataset includes a respective progeny genomic differentiation object. In some of these embodiments, progeny VDAX pairs from multiple progeny VDAXs are processed in a Virtual Binary Language Script (VBLS) only if the respective progeny genomic differentiation objects of the progeny VDAX pairs are sufficiently correlated. can be replaced.

In some of these embodiments, the first progeny genomic differentiation object of the first progeny VDAX of the set of progeny VDAX is updated and the second progeny genomic differentiation object of the second progeny VDAX of the set of progeny VDAX is updated to If not updated, future VBLS exchanges can be prevented.

In some embodiments, the progenitor genomic differentiation object and each progeny genomic differentiation object are XNA objects. In some of these embodiments, the digital ecosystem is a static ecosystem in which the ecosystem platform is configured according to a directed architecture, an interactive ecosystem in which the ecosystem platform is configured according to a free-form architecture, or an ecosystem platform is at least one of the dynamic ecosystems constructed according to the dynamic state spontaneous architecture. In some of these embodiments, a set of one or more enclave VDAXs, each enclave VDAX corresponding to a respective digital enclave of the digital ecosystem, the enclave VDAX controlling the genomic topology of the respective enclave. A set of enclave VDAX where each enclave-specific XNA object is assigned. In some of these embodiments, each respective digital enclave includes one or more descendant VDAXs each representing one or more respective cohorts found in the digital enclave, and each VDAX included in the respective digital enclave. A descendant VDAX is assigned a descendant enclave-specific XNA object that is derived from the enclave-specific XNA object of the enclave VDAX and sufficiently correlated with the descendent enclave XNA objects of each of the other descendant VDAXs contained in the digital enclave. In some of these embodiments, each enclave VDAX controls membership to each corresponding digital enclave by assigning enclave-specific XNA objects to cohorts of the digital enclave.

In some embodiments, each enclave VDAX further assigns a respective enclave genome correlation object derived from the progeny correlation object of the progeny genome dataset. In some of these embodiments, each progeny VDAX of a digital enclave is assigned a respective progeny enclave-specific genomic correlation object derived from the enclave from the respective enclave genomic correlation object of the digital enclave's enclave VDAX. In some of these embodiments, each progeny VDAX is assigned its respective enclave-specific genomic correlation object directly from the enclave VDAX of the digital enclave. In some embodiments, each progeny VDAX allocates its respective enclave-specific genomic correlation object directly from the ecosystem VDAX.

In some embodiments, each progeny VDAX uses its respective progeny enclave-specific genomic correlation object to establish each unique, non-repeating engagement with other progeny VDAXs formed with respect to their respective digital enclaves. produce links. In some embodiments, each link spawned by a descendant VDAX has a unique link-hosting descendant VDAX that defines how the link-hosting descendant VDAX modifies its enclave-specific XNA objects to produce a non-repeating VBLS that is only readable by the descendant VDAX. Provides genomic regulatory instructions. In some embodiments, each descendant VDAX uses its respective descendant enclave-specific genomic correlation object to host links provided by other descendant VDAXes in the digital ecosystem, and other descendant VDAXes are A link is provided to the descendant VDAX to establish a unique non-recursive engagement with the descendant VDAX for each digital enclave.

In some of these embodiments, each hosted link by a progeny VDAX is a non-recurrent VBLS that the progeny VDAX has modified its enclave-specific genomic differentiation object to be decipherable only by the other progeny VDAX that provided the link. provide unique genomic regulatory instructions that define how to generate

In some embodiments, an enclave VDAX controls genomic network topology by selectively updating progeny enclave-specific genomic data objects for a subset of progeny VDAXs that participate in the digital enclave. In some embodiments, the enclave VDAX controls the genomic network topology of the digital enclave without requiring modifications to the physical network topology of the digital enclave. In some embodiments, the digital ecosystem comprises multiple genome topologies overlaying one or more physical network topologies, wherein the multiple genome topologies exist concurrently and interoperably. In some embodiments, the ecosystem VDAX builds and controls genomic network topologies that support applications with dynamic state attributes.

In some embodiments, the system further comprises a set of one or more enclave VDAXs, each enclave VDAX corresponding to a respective digital enclave of the digital ecosystem, the enclave VDAX being the ecosystem designator of the digital ecosystem. and each enclave-specific genomic dataset that controls the part of the genomic network topology responsible for the process.

In some of these embodiments, the system further includes a set of cohort VDAXes that participate in the digital ecosystem, the set of cohort VDAXes being responsible for specific ecosystem-designated and/or enclave-designated functions and processes of genome network topology. contains one or more cohorts, each controlling a respective portion of the In some of these embodiments, interactions by a set of cohort VDAX are controlled by respective cohort genomic data sets assigned to respective cohort VDAXs of the set of cohort VDAX.

In some embodiments, the set of progeny VDAX comprises the set of cohort VDAX.

In some of these embodiments, the cohort genomic data set for each cohort of the set of cohort VDAX includes: one or more cohort genome eligibility objects containing one or both of one unique CNA object and one unique PNA object; one or more cohort genome correlation objects containing one or more LNA objects, each LNA object corresponds to each enclave into which the cohort VDAX is admitted; and one or more cohort genomic differentiation objects containing one or more XNA objects, each XNA object into each enclave into which the cohort VDAX is admitted. Contains the corresponding cohort genome differentiation object.

In some of these embodiments, the digital ecosystem is a dynamic ecosystem, and the ecosystem security platform ensures its operational integrity regardless of how often one or more metric states of the dynamic ecosystem are updated. Constructed according to a self-sustaining architecture.

In some of these embodiments, the ecosystem VDAX and progeny VDAX collectively control the genome network topology in response to certain dynamic metric conditions. In some embodiments, the genome digital network topology is constructed to achieve a controlled level of interoperability. Some embodiments support multiple genomic network topologies overlaid on a co-existing physical network topology.

In some embodiments, the progenitor genomic differentiation object and each progeny genomic differentiation object are ZNA objects. In some of these embodiments, the digital ecosystem is a virtual trusted execution realm implemented on devices having one or more processors, and the offspring VDAX is one or more hardware components and one or more digital one or more hardware components, including one or more of a system-on-chip (SoC), core, or disk; one or more digital components; includes one or more of an OS, processes, threads, libraries, or application programming interfaces (APIs). In some of these embodiments, a descendant VDAX from multiple descendant VDAXs is configured to perform component binary isolation (CBI) with respect to a virtual trust execution domain.

In some embodiments, the system further includes a set of progeny VDAX. In these embodiments, each descendant VDAX is a respective functionally matching module each descendant instance of the ecosystem security platform is configured to perform one or more information theory-facilitated computational complexity functions. Configured with each descendant instance of the ecosystem security platform as configured in a set.

In some of these embodiments, the set of modules of each offspring instance of the ecosystem security platform manages the genomic datasets of the offspring VDAX and performs a set of genomic processing based on the genomic datasets. Contains DNA modules that are composed. In some embodiments, the set of modules includes a link module configured to facilitate secure exchange of links with another VDAX to facilitate unique bisymmetric engagement.

In some embodiments, the set of modules includes a sequence mapping module configured to genomically process sequences (sequences) to derive genomic engagement factors having a particular entropy, wherein the genomic engagement factors are non- Used to encode a digital object in a recursive manner, the sequence is at least one of a public sequence or a private sequence. In some of these embodiments, the set of modules includes a binary conversion module configured to encode digital objects into VBLS objects based on genomic engagement factors determined by the sequence mapping module, each VBLS The object includes an encoded digital object and metadata indicating the public or private sequences used to generate the respective genomic engagement factors used to encode the encoded digital object. In some of these embodiments, the binary conversion module is further configured to decode the received encoded digital objects contained in the received VBLS objects based on their respective recreated genomic engagement factors. be.

In some embodiments, an ecosystem instance of the ecosystem security platform is composed of a respective set of modules each configured to perform a respective set of information theory-based computational complexity functions. . In some of these embodiments, each set of information theory-based computational complexity functions includes cryptographic-based functions, cryptographic-free functions, and at least one stage performed using cryptographic-based functions and cryptographic-free functions. is selected from hybrid functions that include at least one step performed with a function of . In some of these embodiments, the set of modules of the ecosystem instance includes a root DNA module that manages the ancestral genome dataset and generates the progeny genome dataset.

In some embodiments, the digital ecosystem is a cloud service system. In some embodiments, the digital ecosystem is an enterprise information technology system. In some embodiments, the digital ecosystem is a computing device, and each descendant VDAX corresponds to hardware and digital components of the computing device. In some embodiments, the digital ecosystem is a classified computing infrastructure. In some embodiments the digital ecosystem is a traffic grid. In some of these embodiments the traffic grid is an air traffic control grid. In some of these embodiments the traffic grid is an autonomous vehicle traffic grid.

According to some embodiments of the present disclosure, a method is disclosed for managing a set of digital entities in a digital ecosystem. The method includes generating, by a processing system of the ecosystem VDAX, a progeny genome dataset having a particular entropy, wherein the progeny genome dataset has been assigned to the ecosystem VDAX. The method further includes generating, by the processing system, a plurality of different progeny genome datasets each exhibiting a particular entropy. The method also includes, for each offspring of the plurality of different offspring genomic data sets, assigning, by the processing system, the offspring genomic data set to a respective digital entity of the set of digital entities, the set of digital entities corresponding to each digital Fine control of differences and correlations based on the entity's respective progeny genome datasets can be achieved.

In some embodiments, any pair of digital entities in the set of digital entities confirms the correlation of the respective genomic dataset of the pair of digital entities, and the confirmed correlation of the progeny genomic datasets of the pair of digital entities. is configured to distinguish each progeny genome dataset from any other progeny genome dataset based on , forming a unique non-recursive relationship within the digital community. In some of these embodiments, each pair of digital entities is configured to independently ascertain correlations using a particular set of information theory-enhanced computational complexity functions.

In some embodiments, the set of information-theoretically facilitated computational complexity functions includes cryptographic-based functions, cryptographic-free functions, and at least one cryptographic-based function and at least one cryptographic-free function. It is one of the hybrid functions. In some embodiments, each digital entity of the set of digital entities independently distinguishes between its respective progeny genomic datasets using a second set of information-theoretically-enhanced computational complexity functions. configured as

In some of these embodiments, the second set of computationally complex functions are cryptographic-based functions, cryptographic-free functions, and hybrid functions including at least one cryptographic-based function and at least one cryptographic-free function. one of the functions.

In some of the embodiments, in response to forming a unique acyclic relationship, the pair of entities is a unique acyclic virtual binary entity that is only decodable by the pair of entities based on the differentiated progeny genomic data. Engage by generating and exchanging language scripts (VBLS). In some of these embodiments, the VBLS is composed of encoded digital objects carrying information-theoretically driven genomic attributes of respective genomic datasets of a pair of digital entities.

In some embodiments, the specified entropy is a configurable level of entropy defined by community owners associated with the ecosystem VDAX. In some embodiments, digital entities collectively enable virtual authentication, virtual affiliation, and virtual agility.

In some embodiments, the progeny genomic data set includes a genomic correlation object exhibiting a particular entropy and a genomic differentiation object exhibiting a particular entropy. In some of these embodiments, each progeny genome data set includes each genome qualification object exhibiting a particular entropy.

In some embodiments, the digital ecosystem includes one or more digital enclaves formed of respective inter-interests represented by controlled differences and correlations in progeny genomic datasets. In some of these embodiments, each digital enclave contains one or more cohorts that share respective mutual interests represented by controlled differences and correlations in progeny genomic datasets.

A more complete understanding of the present disclosure will be appreciated from the following description and accompanying drawings, and from the claims.

The accompanying drawings, which are included to provide a better understanding of the disclosure, illustrate embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 is a schematic diagram illustrating features of a ciphergenic-based digital ecosystem in relation to an organic ecosystem and a modem digital ecosystem, according to some embodiments of the present disclosure.

FIG. 2 illustrates a cyphergenics-enabled security stack that can be applied in coexistence with commonly known application and network stacks, and the digital ecology that can result from such application, according to some embodiments of the present disclosure. FIG. 10 shows an example of a ciphergenics-enabled genomic architecture of the system.

FIG. 3 illustrates attributes of ciphergenics-based technology in relation to attributes (and potential shortcomings) of current and developing security-related technologies, according to some embodiments of the present disclosure.

FIG. 4 is a diagram illustrating an exemplary configuration of a ciphergenics-based ecosystem security platform, according to some embodiments of the disclosure.

FIG. 5 is a diagram showing an exemplary implementation of genomic datasets, according to some embodiments of the present disclosure.

FIG. 6 is a diagram illustrating an example of a ciphergenics-enabled digital ecosystem governed by a set of CG-enabled VDAX, according to some embodiments of the present disclosure.

FIG. 7 is a diagram illustrating an exemplary implementation of security platform instances configured according to a directed architecture that supports a static ecosystem, according to some embodiments of the present disclosure.

FIG. 8 is a diagram illustrating an exemplary implementation of a security platform instance configured according to a free-form architecture supporting a transient ecosystem, according to some embodiments of the present disclosure.

FIG. 9 is a diagram illustrating an example implementation of a security platform instance configured according to a voluntary architecture supporting a real-time/dynamic ecosystem, according to some embodiments of the present disclosure.

FIG. 10 is a diagram illustrating an exemplary implementation of a security platform instance configured according to a transient architecture supporting virtual trusted execution domains, according to some embodiments of the present disclosure.

FIG. 11 is an illustrative process for forming a unique non-recursive engagement to exchange data and securely exchanging data based on the unique non-recursive engagement, according to some embodiments of the present disclosure. Fig. 3 shows an implementation;

Figures 12 and 13 illustrate examples of different CG-enabled digital ecosystems that may be formed according to some embodiments of the present disclosure. Figures 12 and 13 illustrate examples of different CG-enabled digital ecosystems that may be formed according to some embodiments of the present disclosure.

Hyperscalability (e.g., having the ability for N digital populations to directly establish mutual identity of interest exhibiting high entropy with N different digital populations) is important for all network-centric It is presented to be the missing link to comprehensive security of dynamic state virtualization of digital ecosystems. Hyperscalability (e.g., many-to-many), along with comprehensive security, has the potential to drive entirely new network-centric virtualization products and services.

As described herein, Cyphergenics-enabled hyper-scalability requires minimal additional overhead or bandwidth, while N×N engagement-instances may grow to Nx×NY , is equally valid and computationally robust across all network and application stack levels. In embodiments, the results of hyperscalable direct digital-to-cohort virtual authentication and virtual alliances are similar to hyperscalable biological intergene virtual correlations and virtual differentiation, even though the underlying technologies on which each is achieved are very different. is the result of The same is true for other digital attributes that CG hyperscalability enables (eg virtual agility, virtual data objects, virtual code objects).

The description of ciphergenics is a fully digitally-enabled technology, but a fully biochemically-enabled technology that is probably, and hopefully more familiar to, all but a few at the moment. Substantial assistance is provided by the adoption of accompanying genomic terminology. Although specific biochemical processes have not technically impacted ciphergenics, their ability to address similar challenges and levels of complexity will benefit ciphergenics' basic proposition after the fact. added. Non-limiting examples in which cipherogenic terms and computationally complex digital functions and processes and biochemical-based functions and processes share particularly correlated genomic representations include:

Genomic Information: A numerical, narrative, or other such script whose elements collectively exhibit little or no computationally separable order or relationship.

Genomic entropy: The degree to which it is possible to computationally assess and confirm the extent to which there are no recurring or predictable patterns in genomic information.

Genome construction: The ability to permute or reconstruct genomic information from its original sequence or relational basis into specific subsets without loss of relative entropy.

Genome Modification: The ability to process genomic information based on computationally complex functions and processes, whose computational properties are provable, if not observable, and consistent across modified genomic information bases. ing.

Genomic Regulation: The ability to conditionally and temporarily modify the complete genomic information base or a specific subset(s) to achieve a specific purpose (e.g., digital cohort fine-tuned revocation), when ( That is, prior knowledge of the current base (after the change) is required.

Genome Revision: The ability to derive subsets of genomic information by rearranging, altering, or adjusting digital data objects and digital code objects.

This disclosure relates to various embodiments of the Ciphergenics Ecosystem Security Platform (also referred to as “CG-ESP,” “Security Platform,” or “Genome Security Platform”) and the processes and techniques facilitated by the CG. In embodiments, CG-ESP provides computational resources and process control where genome information, genome entropy, genome construction, genome modification, genome regulation, and genome revision uniquely work together to achieve computationally complex hyperscalability. do. In embodiments, hyperscalability, in turn, enables digital ecosystems, enclaves, and digital cohorts to achieve virtual authentication, virtual affiliation, virtual agility, virtual sessionless engagement, and/or virtual execution space attributes. Enable genome-based engagement. In some embodiments, these attributes in turn facilitate application-specific genomic network security topologies without replacement or reconfiguration of hardware connections.

In embodiments, an instance of a CG-ESP consists of all or part of a particular information-theoretically constructed genomic attribute (e.g., an ecosystem, an enclave, and/or a digital cohort (or "cohort")). It may be parameterized with a digital genome dataset that reflects one or more mutual identities of interest for a particular digital community). In embodiments, a parameterization of CG-ESP instances with genomic attributes constructed by a particular information theory constitutes a respective Virtual Anonymous Exchange Controller (or "VDAX"), which is executed by the processing system. It can enable VDAX to play a role within their respective digital communities. In embodiments, a CG-ESP can enable multiple VDAXs, albeit with different or overlapping mutual identities of interest.

In embodiments, the CG-ESP may be configured to build and manage digital correlation and differentiation functions on behalf of the digital ecosystem or its constituents. In embodiments, a digital ecosystem may refer to a digital community having one or more enclaves each having a mutual identity of interest. In embodiments, an enclave may refer to a collection of one or more cohorts with mutual identities of interest. In embodiments, the term "cohort" may refer to independent cohorts and/or dependent cohorts. In some embodiments, an independent cohort may refer to a collection of one or more devices operating as independent entities. In some of these embodiments, independent cohorts may include, but are not limited to, grids, networks, cloud services, systems, computers, appliances, devices, and IoT devices. A dependent cohort may refer to an individual digital entity enabled by an independent cohort acting on behalf of the individual digital entity. Examples of dependent cohorts include, but are not limited to, sensors, apps, data, files, and content. As explained, the designation of independent and dependent cohorts may vary across different types of architectures and ecosystems. For example, according to some embodiments of the temporal architecture (described below), certain device components (e.g., processors, processor cores, cameras, etc.) and software instances may be designated independent cohorts and other device components and software instances may be designated dependent cohorts. Note that in some embodiments, these types of designations may be determined by community owners associated with the digital ecosystem.

In an exemplary embodiment, the Ciphergenics-based Ecosystem Security Platform (“CG-ESP”) forms an ecosystem with one or more enclaves, independent and dependent cohorts with mutual identity of interest. manages the membership of the collection of In embodiments, GC-ESP has one or more core capabilities, such as platform capabilities to control and manage genomic functions and processes, and link exchange capabilities to provide the means by which link data (e.g., genome engagement cargo) are exchanged. I will provide a. In embodiments, mutual identities of interest may be defined according to any logical commonality between cohorts within an enclave, which may be defined by or on behalf of a community owner. good too. For example, are there mutual identities of interest among user devices, servers, printers, documents, applications (e.g. cohorts) that form business units (e.g. enclaves) within an enterprise organization (e.g. digital ecosystem)? may In another example, a home network may have one or more enclaves (e.g., a work-related enclave used for a home office and a personal enclave for personal or family devices, applications, and files). , user devices, smart devices, gaming devices, sensors, wearable devices, files, and applications (eg, cohorts) operating on the home network (eg, ecosystem). Another example is mutual interaction between autonomous vehicles (e.g. cohorts) running on a particular grid (e.g. enclave) of a smart transportation system (ecosystem) managed by a local authority (e.g. community owner). Identical interests may exist. The above are non-limiting examples of ecosystems, enclaves, cohorts, and identities of interest, and many other examples will be described throughout this document. Furthermore, since digital entities may be considered cohorts in a first ecosystem (e.g., mobile devices in an enterprise ecosystem), digital entities may play different roles within an ecosystem or across multiple ecosystems. Note that there is For example, a mobile device within an enterprise ecosystem may be considered an enterprise ecosystem cohort, but may define an entire digital ecosystem within an executable ecosystem.

As explained in more detail, the composition of the CG-ESP can be defined by community owners of the digital ecosystem. When referring to “community owner” throughout this disclosure, the term refers to the entity (e.g., corporation, organization, government, individual human being, etc.) that manages, maintains, or owns the community and/or its representatives (e.g., network administrator, CIO, IT administrator, family member, consultant, security professional, artificial intelligence software acting on behalf of a community owner, or any other appropriate representative). Further, in some embodiments, the CG-ESP may be pre-configured and sold to community owners, whereby the community owners can make decisions regarding community membership and/or the functionality of the CG-ESP. It may be determined whether or not gender decisions (eg, which CG-ESP modules and configurations are used in CG-ESP) can be made.

In the context of biology, core biological genomic competencies, including biological differentiation and correlations, provide convenient correlations to describe the formulation of CG-ESP digital processes. However, any reference to or derivation of "genomic" cohorts (e.g., genomic datasets, DNA, sequence mapping, mutation, cloning, and/or the like) in the context of ciphergenics-related technology does not imply that these processes are biological It is to be understood that it is not intended to imply that it mimics or inherent in any or all specific characteristics of the genome assembly or process. In embodiments, CG-ESP performs genomic processes that can include digital generation, modification, corroboration, and/or assignment of particular types of genomic data. In embodiments, these genomic processes and data allow computation of differences and correlations that indicate user-controlled entropy. In these embodiments, these digital genome processes rely on configurations driven by specific information theories. In some embodiments, these constructs may be referred to as digital DNA (or genomic data). In embodiments, the digital DNA is LNA (genomic correlation), CNA (genome engagement-completeness), PNA (genome engagement-qualification), XNA (genomic differentiation), and/or ZNA (genomic code separation/cloaking), etc. may include one or more information-theoretically facilitated structures of As explained, these cyphergenics-based (or “CG-based”) processes and structures facilitate hyper-scalability across a wide range of digital ecosystems. Examples of CG-based processes may include, but are not limited to, CG-based linking processes, CG-based sequence mapping, and/or CG-based transformations, examples of which implementations are described throughout this disclosure. .

In embodiments, the genomic digital link (or "link") is the virtual digital anonymous exchange controller ("VDAX") (described further below) required to perform higher-level computationally complex genomic functions. Allows the exchange of information. In embodiments, CG-based genome link processing may include link generation, link hosting, and/or link updating, implementation examples of which are described throughout this disclosure. These CG-based linking processes provide attribute-specific genome assembly information such as LNA (genome correlation), CNA (genome engagement-completeness), and PNA (genome engagement-qualification).

In embodiments, CG-based sequence mapping can refer to the following techniques. Computational transformation of digital sequences (e.g., published or private protocol sequences) into genomic engagement factors. In embodiments, these genomic engagement factors may be unique and non-repetitive. Different types of sequences may be broadly heterogeneous, but the sequences may be processed in ways that result in genomic engagement factors exhibiting particular levels of entropy. In embodiments, a CG-based sequence mapping process compatible with a wide range of protocols and formats may be initiated on existing entropy-indicating sequences, whereby each sequence is transformed by a computationally complex CG-function. , are processed into unique genomic engagement factors. These genomic engagement factors may be used to encode digital objects into VBLS.

As described, embodiments of CG-ESP can facilitate many hyper-scalable attributes not possible with modem cryptography and related security systems. These attributes are virtual affiliation (infinite difference), virtual authentication (infinite correlation), virtual agility (infinite structural adaptability), and virtual engagement (sessionless control per discrete data object), and virtual trust execution. It may include, but is not limited to, VBLS (Virtual Binary Language Script) that allows domains (execution control per discrete code object). The term "unbounded" as used herein means unbounded in any practical sense, recognizing that it is theoretically possible to describe "bounded" scenarios. should be noted.

Hyperscalability: In some embodiments, hyperscalability may refer to the ability to globally associate N cohorts (or other community members) with M points of contact across T instances (M× NxT). Given that billions of potential cohorts will have countless contacts and communicate across countless instances, hyperscalability of this magnitude requires fundamental breakthroughs in modem cryptography. The CG-based system described herein provides a significant reduction in computational costs and session state. In embodiments, these significant reductions come at the expense of relatively insignificant overhead and/or bandwidth.

virtual authentication. In embodiments, virtual authentication of ecosystem members (eg, ecosystems, enclaves, cohorts, etc.) may require hyperscalability techniques. In embodiments, hyperscalability techniques enable engagement of ecosystems, enclaves, and/or cohorts where precise and unique correlations (eg, “who is who”) may be required. In some of these embodiments, an accurate and unique correlation is that a digital community (e.g., cohort, enclave, ecosystem, etc.) uniquely identifies another member (e.g., another cohort, enclave, ecosystem, etc.). may refer to a specific set of information-theoretically driven genomic processes that validates In embodiments, virtual authentication may refer to the ability to authenticate an unlimited number of ecosystem members (eg, ecosystems, enclaves, cohorts, etc.). As discussed, a CG-enabled ecosystem may achieve infinite correlations to ecosystem members (e.g., enclaves, cohorts, subordinate cohorts), which in turn leads to an infinite amount of correlations to be formed. Provide a unique relationship. In some embodiments of the present disclosure, cohorts from different ecosystems may also be configured to authenticate each other in an unlimited manner. As explained, infinite correlations are achieved by genomic information theory-enhanced constructions and processes (also referred to as "cyphergenics-based" or "CG-based" or "CG-enabled" constructions and/or processes). may

Virtual Affiliation: In embodiments, virtual differentiated engagement between ecosystems, enclaves, and cohorts may require hyperscalability. Hyperscalability technologies will enable ecosystem, enclave, and cohort engagement for most, if not all, scenarios where precise and unique differentiation (“what”, “only us”) may be required. enable. In embodiments, accurate and unique differentiation is performed by a unique pair of community members, matched or sufficiently matched, to establish a unique engagement that differentiates the pair from any other community member. may refer to a set of processes that have In some of these embodiments, such accurate and unique differentiation ensures that unintended digital entities cannot participate in uniquely established engagements (e.g., decrypt intercepted data, etc.). do. In embodiments, hyperscalable differentiation is the ability of members of an ecosystem to uniquely partner with an infinite number of members of other ecosystems (e.g., ecosystems, enclaves, cohorts, and/or the like). may refer to Organic ecosystems demonstrate, albeit limited, strong differentiation between species, progeny and siblings resulting from complex biochemical processes. However, borderless differentiation may be possible through digital construction and processing based on genome information theory. As described, a CG-enabled ecosystem may achieve unlimited differentiation for members of the ecosystem (e.g., enclaves, cohorts, subordinate cohorts), which in turn allows unlimited differentiation to be formed. provides a unique relationship in the amount of In some embodiments of the present disclosure, cohorts from different ecosystems may also be configured to form unique relationships in unlimited ways. As explained, infinite differentiation can be achieved by genomic information theory-governed constructions and processes (also referred to as "cyphergenics-based" or "CG-based" constructions and/or processes).

Virtual Agility: In embodiments, virtual agility within an ecosystem, enclave, and/or cohort platform stack(s) may be enhanced by hyperscalability. Hyperscalability techniques enable ecosystems, enclaves, and cohorts to agilely perform hyperscalable differentiation and hyperscalable correlation on software and hardware managed processes. In some embodiments, agile execution of software- and/or hardware-managed processes can be implemented using a variety of respective protocol stacks (e.g., OSI-network stacks, software stacks, processing stacks, and/or the like). May refer to processes applicable at a level. Organic ecosystems demonstrate a limited but powerful agility at the cellular level controlled by complex biochemical processes. However, in some cases, unbridled agility can be achieved through digital construction and processes based on specific genomic information theories.

Virtual Binary Language Script (VBLS): Engagement of ecosystems, enclaves, and/or cohorts with Virtual Binary Language Script enabled requires hyperscalability. Hyperscalability technology enables ecosystems, enclaves and cohorts to engage via a unique, non-recurring, computational quantum proof binary language (or non-quantum proof binary language if community owners so desire) can. Organic ecosystems demonstrate powerful, defined, and unique cellular interactions based on complex biochemical processes. However, a digital structure based on a particular genomic information theory may achieve unlimited unique digital object engagement.

Virtual Trust Execution Domain: In some embodiments, a well-configured CG-ESP employs computationally complex genomic organization and processes to uniquely transform engagement for viable ecosystem components. enable the process to In embodiments, an executable ecosystem may refer to different software and hardware components of a device (or system of interdependent devices operating as a single unit). In embodiments, executable ecosystem components include, for example, application components (APIs, libraries, threads), operating system components (e.g., kernel, services, drivers, libraries, etc.), and system-on-chip (Soc) components ( processing units (eg, cores), hardware components (eg, disks, sensors, peripheral devices, and/or the like), and/or other suitable types of components. In some embodiments, these ecosystem components (e.g., specific designations and organizations such as ecosystems, enclaves, and cohorts) jointly or independently process (e.g., encode and/or decoding).

Genomically-enhanced Virtual Network Architecture: In embodiments, the genomic processes and capabilities of CG-EPS enable the inversion of relevant protocols for application security and network architecture, regardless of the unique demands of a particular use case. . In some embodiments, the disclosed "genome network topology" technology enables the creation of entirely new use-case-specific security architectures. In some embodiments, a single physical network topology can simultaneously support multiple genomic topologies. As used herein, genome topology or genome network topology may refer to the topology of a digital ecosystem defined using the genomic structure of each member of the digital ecosystem.

In embodiments, ecosystems, enclaves, and their membership may be defined by digital community owners. For example, a network administrator affiliated with an enterprise can set up security platform instances that establish respective enclaves for different units or projects of the enterprise. In this example, a network administrator may configure the security platform instance to add cohorts to one or more enclaves based on the cohort's capabilities. Note that in some embodiments, a cohort may be contained in multiple enclaves, and an enclave may have overlapping cohorts. Further, in some embodiments, multiple cohorts are computing devices and various hardware (e.g., CPUs, GPUs, memory devices) and/or software components (operating systems, file systems, applications, files), etc. May be associated with a single device. As described, CG-ESPs may be configured to form many different types of ecosystems, with membership configurable and defined by community owners and/or CG-ESP providers. good too. In embodiments, the security platform is configured to “genomically” construct heterogeneous functions, systems, and/or theaters of operation based on mutual identity of interest. Stated another way, in these embodiments, the CG-ESP is the (e.g., by a community owner or similar party) of community members (e.g., enclaves, ecosystems, and/or cohorts) within a digital community. It may be constructed and operated to control the genome network topology of the digital ecosystem using genome construction. In this way, establish community members, add them to a particular enclave or ecosystem, withdraw them from a particular enclave or ecosystem, etc. by altering the genomic structure of one or more members within the digital community be able to.

In some embodiments, a ciphergenics-based ecosystem security platform (CG-ESP) may refer to a set of CG-enabled modules that perform various CG-based functions on specific configurations of genomic data, A CG-ESP instance is a CG-ESP platform with a composition of CG-enabled modules that depends on the role the CG-ESP instance is performing with respect to the community (e.g., ecosystem level, enclave level, cohort level, dependent cohort level). May refer to an instance. In some embodiments, a CG-ESP platform instance may be embodied as a VDAX. In these embodiments, the VDAX may perform a specific configuration of CG-enabled modules defined for the VDAX role. Examples of VDAX Roles may include an Ecosystem VDAX, an Enclave VDAX, a Cohort VDAX, and/or a Dependent VDAX, whereby each of these VDAX has a CG-enabled operation required by the CG-ESP module and role. can be configured according to In embodiments, a CG-ESP instance has one or more core capabilities that may include control and management of genome construction, functions, and processes (platform capabilities) and/or secure data exchange functions and processes (link exchange capabilities). may provide. In embodiments, an ecosystem VDAX may perform security-related functions on behalf of the ecosystem and may be considered an "ancestor" of the ecosystem. In some of these embodiments, one or more corresponding enclaves VDAX may be configured to perform security-related functions on behalf of their respective enclaves. In embodiments, a cohort VDAX may perform genome security-related functions on behalf of each independent cohort within the ecosystem. In embodiments, subordinate VDAXs may perform genome security-related functions on behalf of their respective subordinate cohorts within the ecosystem. Note that in some embodiments, an independent cohort may host one or more dependent VDAXs on behalf of one or more dependent cohorts that depended on the independent cohort. Additionally, in some embodiments, a single cohort VDAX associated with an independent cohort may be configured to perform security-related functions for the cohort across heterogeneous enclaves and ecosystems. In these embodiments, the cohort VDAX may manage and utilize different genomic datasets on behalf of the cohort according to the respective configurations of different ecosystems and/or enclaves. For example, a mobile device that a user uses for work and personal affairs can access one or more genomic datasets related to the user's work ecosystem and enclave, according to the CG-ESP configuration of the organization the user works for. , and a cohort VDAX that manages and utilizes one or more genomic datasets associated with the ecosystems and enclaves in which the user participates, according to the platform configuration of those ecosystems and enclaves. In some embodiments, one or more enclave VDAXs and/or ecosystem VDAXs may be hosted by the same computing system. For example, large digital ecosystems (e.g. federal or state governments, large corporations, military, autonomous vehicle grids, IoT grids, etc.) ecosystems and enclaves VDAX are distributed cloud computing systems (e.g. AWS, Azure, Google Cloud Services, privately owned server banks, etc.), while small digital ecosystems (e.g., home networks, small office networks, grassroots non-profits, etc.) ecosystems and enclave security controllers can simply It can be hosted on a single computing device (eg, central server, router, mobile device, etc.). Also note that in some exemplary embodiments, VDAX may be configured to play different roles for different ecosystems.

For descriptive purposes, the terms "ancestor" and "descendant" (e.g., "ancestor security controller" or "ancestor VDAX" and "descendant security controller" or "descendant VDAX") will be used to indicate that the "ancestor" VDAX generated a genomic dataset. It may be used to indicate a relationship that may be created, assigned, and/or otherwise provided to a “descendant” VDAX. For example, in some embodiments, an ecosystem VDAX is assigned a unique but correlated genomic dataset derived from an "ancestor" ecosystem VDAX, such that for each enclave, its "descendant" enclave VDAX is assigned one It can revise its own digital genome dataset for more than one enclave. Similarly, in another example, an enclave VDAX generates, assigns, and assigns a unique correlated genomic dataset for a cohort within an enclave such that for each enclave its descendant cohort VDAX is assigned its own genomic dataset. and/or may modify its own genomic data set to serve in other ways. In this way, descendants of an ancestor VDAX (e.g., descendant VDAX) can exchange data in a partially cryptographically secure manner due to the high correlation between their respective genomic datasets. There is Note that in some embodiments of the CG-ESP platform, an ecosystem VDAX may generate and allocate genomic datasets for cohorts of the ecosystem even if an enclave VDAX exists. sea bream. In embodiments, an ancestor VDAX (e.g., an ecosystem or an enclave VDAX) is a role-based CG-ESP platform such that the descendant VDAX is composed of appropriate CG-ESP modules given the descendant VDAX's role with respect to the VDAX. may be provided to descendant VDAXs. As described, the configuration may include respective modules configured with specific cryptographic-based, non-cryptographic, and/or hybrid computational complexity functions used in the described CG-based processes. good. In embodiments, a cryptographic-based function may refer to a function performed in which all stages (one or more stages) of the function are performed using a key-based reversible transform (e.g., symmetric cryptography) . Examples of key-based reversible functions include Advanced Encryption Standard (AES), SAFER+, Serpent, Twofish, RC6, MARS, Camelia, MISTY! , SHACAL-2, Triple-DES, SAFER++, HC-138, Rabbit, Sasa20/12, SOSEMANUK, Grain, MICKEY, Trivium, and/or any other suitable key-based reversible now known or later developed Including but not limited to functions. In embodiments, a crypto-free function may refer to an executed function in which none of the stages of the function are executed using a key-based reversible function. In embodiments, a hybrid function may refer to a performed function that includes at least one step performed with a cryptographic-based function and at least one step performed with a cryptographic-free function. . For example, a hybrid function may include a first stage in which a crypto-based function is used to determine an intermediate value, and a second stage in which a crypto-less transforms the intermediate value to an output value, which is reversible It may or may not be.

In some embodiments, the CG-ESP is configurable by the ecosystem owner or the ecosystem owner's agent. As mentioned above, CG-ESP is a set of interdependent modules that collectively perform one or more genomic security functions such that any level VDAX contains instances of some or all of the interdependent modules. may contain. These interdependent modules are executed by conventional processing devices (e.g., CPUs or GPUs and/or FPGAs, microprocessors, or special purpose chipsets) specially configured to perform specific genomic functions. Note that it may be implemented as a possible instruction. Stated another way, the interdependent modules of a particular security controller instance (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, dependent VDAX, and/or the like) are individually software, middleware, firmware, and/or It may also be embodied as hardware. References to a processor, execution, or the like are meant to apply to any of these configurations, unless the context dictates otherwise. In embodiments, individual modules of a particular CG-ESP instance operate on a particular set of different types of genomic data objects (e.g., CNA, LNA, XNA, PNA, ZNA, etc.) and/or It may be configured to perform different types of functions and strategies. For example, some modules may be configured to apply cryptographic-based computational complexity functions to genomic data objects and/or digital data generated or leveraged in connection with genomic security operations. Examples of cryptographic-based computational complexity functions include Advanced Encryption Standard (AES) encryption/decryption, SAFER+ encryption/decryption operations, unique privately developed encryption/decryption operations, and/or similar Examples include, but are not limited to. Additionally or alternatively, in some embodiments, some of the interdependent modules apply crypto-less computational complexity functions to genomic data objects and/or digital data generated or leveraged in connection with genomic security operations. may be configured to Examples of crypto-less computational complexity functions may include, but are not limited to, cryptographic hash functions, parametric linear equation-based transforms, multivariate equation-based transforms, grid-based transforms, and the like. Additionally or alternatively, in some embodiments, some modules are hybrid (e.g., crypto-based and crypto-less ) may be configured to apply a computational complexity function. As described, hybrid functions can include some combination of cryptographic-based and non-cryptographic functions. As described, a CG-ESP may be configured (eg, by or on behalf of a community owner) according to the needs and limitations of the digital community it serves.

In embodiments, the CG-ESP is one or more digitally generated genome structures that can be embodied in objects exhibiting composable entropy (e.g., binary matrices, binary vectors, primitive binary polynomials, etc.). It is configured for secure end-to-end data exchange between ecosystem members using specific genomic datasets, including In embodiments, these digitally generated mathematical objects are generated using a series of CG-based processes to exploit the high correlation and differentiability between the respective genomic datasets of ecosystem members. to securely exchange digital objects between any pair of well-correlated ecosystem members. In embodiments, hyperscalable genomic correlation provides an infinite number of genomic progeny that can be directly authenticated as members of a fellow enclave or ecosystem without the support of out-of-bounds trusted services (e.g., certificate authorities, private key exchange, etc.). It can provide the ability to have a community. In some embodiments, hyperscalable differentiation is performed by linking two well-related cohorts to a virtual binary language script (VBLS) (each instance of which is a VBLS object may refer to the ability to generate and exchange In an embodiment, the link is transferred to the second VDAX to generate a VBLS that can be decoded by the VBLS (assuming the link is held securely by the second VDAX). digitally encoded instructions (which may be referred to as "genome regulatory instructions" or "GRI") from one VDAX to another that define how the XNA or ZNA) should be modified. contains. In embodiments, a second VDAX may "host" a link that indicates a GRI corresponding to the first VDAX, whereby the second VDAX modifies its genomic dataset based on the GRI. may also generate VBLS that are readable by the first cohort based on the corrected genomic data. In embodiments, a second VDAX maps the sequences to the corrected genomic data to obtain genomic engagement factors, which in turn convert digital objects contained in VBLS objects (e.g., disambiguation and/or using encryption techniques). In embodiments, a VBLS object may be a data container that includes one or more encoded digital objects and metadata used to decode the encoded digital object(s). In an embodiment, the first VDAX receives the VBLS object, uses the GRI provided to the second VDAX to modify its own genome dataset, and performs genome engagement based on the modified genome dataset. Decode the digital object from the VBLS by determining a factor and decoding the encoded digital object based on the genomic engagement factor. As described below, only the first VDAX can decode the digital object from VBLS, and other cohorts (members of the digital community or others) who do not have access to the GRI contained in the linking information cannot decode the digital object encoded in VBLS. digital object cannot be decrypted.

As described, in embodiments, CG domain components (e.g., ecosystems, enclaves, independent cohorts, and/or dependent cohorts) are digital ecosystem CG-enabled components that are crypto-based, crypto-free, and/or Alternatively, it may be constructed with digital genome datasets such that precise control of differences and correlations may be achieved using information theory-facilitated computational complexity functions, which may be hybrid functions. In embodiments, CG-enabled methods may support CG-based genomic processes that allow dynamic specification of entropy. In embodiments, the CG domain component may engage according to information-theoretic genomics (ciphergenics) and utilize information-theoretic genomics that allows for virtual authentication, virtual scalability, and/or virtual agility. In an embodiment, the CG component generates VBLS so that the CG-enabled process allows two domain components to construct and engage via a native non-reproducing digital language (e.g., VBLS). Configured. In embodiments, CG components provide the ability to establish and control differences and correlations between CG domain components, which may enable extensive scalability (eg, hyperscalability). In embodiments, CG domain components engage via a specific digital protocol composed of digital objects whereby digital objects carry the information genomic attributes of their descendent components (e.g., ecosystems or enclaves). do. In embodiments, when exercised at the digital object level, hyperscalability enables agile application of CG-based attributes beyond the component level (eg, at the format and/or protocol level).

As mentioned above, a genomic dataset (also called a "digital DNA set", "DNA set" or "DNA") is one or more that exhibits a particular level of entropy, such that the level of entropy is configurable. can include a digitally generated mathematical construction of As noted above, for purposes of explanation, reference and derivation is made to the concept of biological genes. For example, terms such as DNA, "mutation", "genomic data", "genomic construct", "progeny", "cloning", "sequence (sequence) mapping", etc. are used throughout this disclosure. It should be understood that such references are not intended to ascribe any particular characteristic of biological genetic material or processes to any of the terms used herein. Rather, the terms are used to teach others how to practice various aspects of the disclosure. In embodiments, a genomic dataset may include a genomic eligibility object, a genomic correlation object, and/or a genomic differentiation object.

In embodiments, the Genomic Eligibility Object is digitally generated mathematics that allows cohort pairs to genomically verify engagement eligibility, which can be performed as part of a "trustless" authentication process between two VDAXs. May point to an object. In embodiments, a progeny VDAX (e.g., an ecosystem VDAX) is derived from its genome eligibility objects ("descendant genome eligibility objects") to its progeny such that each progeny receives a unique but correlated genome eligibility object. A genome eligibility object for may be derived. Once assigned a genome eligibility object, descendant VDAXs can receive the genome eligibility object. In some embodiments, offspring VDAX are genomically qualified via a one-time trusted event (e.g., upon ecosystem entry into a particular ecosystem, when a device is manufactured, configured, or sold, etc.). may receive sexuality. After this single trusted event, enough VDAX can independently confirm eligibility for engagement with each other using their respective genomic eligibility objects. In embodiments, genome-qualifying objects may include CNA objects, PNA objects, or other suitable types of mathematical objects that exhibit configurable entropy levels, correlations, and derivatives, which are discussed in more detail below. explained.

In embodiments, a genomic correlation object may refer to a digitally generated mathematical object that allows VDAX to exchange links, whereby the links indicate that a pair of well-correlated VDAX is a digital community. provide indications that allow it to be sufficiently distinguished from other well-correlated VDAXs within. In embodiments, genomic correlation objects are used by VDAX to ascertain link exchange correlations, whereby two ecosystem components (e.g., enclaves and/or cohorts) establish specific relationships and engage with each other. be able to In an exemplary implementation, the genomic correlation object of a community member of the digital ecosystem is an LNA object or any other suitable type of mathematical object that exhibits composable entropy and correlation, which is described in more detail below. explain.

In embodiments, the genomic differentiation object is such that a pair of VDAXs (e.g., an enclave or cohort) is generated by each other VDAX when the VDAX normally hosts a link generated by each other VDAX. It may refer to a digitally generated mathematical object that enables the generation and decoding of VBLS objects. In some embodiments, the first VDAX partially modifies the second VDAX by modifying its genome differentiation object in a manner defined by instructions contained in the hosted link corresponding to the second VDAX. Generating a VBLS for the VDAX and modifying its genome differentiation objects in a manner defined by the instructions contained in the link hosted by the second VDAX with respect to the first VDAX, in part Decrypt VBLS from Examples of genomic differentiation objects may include, but are not limited to, XNA objects, ZNA objects, or any other suitable type of mathematical object exhibiting configurable entropy and correlation, such as: are discussed in more detail.

As will be explained, different combinations and configurations of CG-ESP modules and genomic datasets can be used with different CG-ESPs. Modern network capabilities substantially reflect the underlying deployment architecture. A VBLS-enabled genome assembly architecture that operates at the bit level can maintain interoperability with the underlying deployment architecture. According to embodiments of the present disclosure, VBLS offers unprecedented facilities and flexibility for uniquely tailored use cases, whether network-, software-, or hardware-centric architecture. Examples of different architectures are directed architectures that can be deployed in static ecosystems (e.g. large enterprises), free-form architectures that can be deployed in transitional ecosystems (e.g. social networks, websites). , dynamic ecosystems (e.g., city-wide autonomous vehicle control systems), transient architectures that can be implemented against executable ecosystems (e.g., OS, browsers), and/or positive ecosystems (e.g., , blockchain or other distributed ledgers). In embodiments, these architectures overlay existing physical network topologies, demonstrating genome assembly topologies, multiple genome assembly topologies may exist simultaneously and interoperably. Examples of different architectures and CG-enabled ecosystems are described throughout this disclosure.

FIG. 4 is a diagram illustrating an example CG-ESP 400 according to some embodiments of the disclosure. In these embodiments, the CG-ESP 400 comprises genomic data such that different CG-ESP 400 may contain different CG-modules that perform different genomic functions and associated computational methods that operate on corresponding configurations of genomic datasets. Containing a set of CG-modules configured to perform a set of CG-processes and associated computational methods for a particular configuration of a dataset. In embodiments, the CG-ESP 400 may have different roles (e.g. eco system VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX). In this way, all community members are provided by community members and/or by the community so that the CG-ESP instance is configured for the community member's role (e.g., ecosystem, enclave, cohort, or subordinate cohort). A corresponding CG-ESP instance running on behalf of a member (eg, for a subordinate cohort) may be used to participate in a CG-enabled digital ecosystem. For example, community members acting as ecosystem progenitors (e.g., ecosystem VDAX) generate genomic datasets (digital DNA sets) with one or more specific types of genomic data (e.g., digital DNA sets). It can be configured with a CG-ESP instance that contains a CG module that defines the CG-functions and associated computation methods for . CNA, PNA, LNA, XNA, and/or ZNA) consisting of a CG-ESP instance that includes a CG module that defines CG functions and associated computational methods for generating genomic datasets (digital DNA sets), On the other hand, community members, who are independent cohorts within the ecosystem (e.g., principally VDAX), have CG modules that define CG-processes and computational methods that transform their genomic data, link exchanges, sequence mappings, transforming digital objects, etc. may consist of In this way, different community members of the CG-enabled ecosystem can run different instances of one CG-ESP 400. Note that in some embodiments the modules of an instance of CG-ESP 400 are executed by the VDAX of the digital ecosystem that the CG-ESP instance supports. Furthermore, different classes of VDAX in a CG-enabled ecosystem may use some or all of the modules of CG-ESP, as different types of VDAX within a particular digital ecosystem may play different roles within the digital ecosystem. Note that for each module in practice, different classes of VDAX of CG-ESP instances can be configured to perform some or all of the genomic functions of the CG module. Also, different classes of VDAX within the digital ecosystem may be configured to play different respective roles within the digital ecosystem, but all VDAX configured to perform certain CG-processes with respect to the digital ecosystem. (e.g., XNA and LNA modification, link exchange processing, VBLS generation/decryption, and/or ibid.) are functionally identical functions (e.g., identical cryptographic-based, cryptoless , or hybrid function, function to extract the same sequence, etc.). In this way, well-related VDAXs can perform specific CG-processing in a functionally consistent manner, whereby well-related VDAXs can, for example, determine engagement qualifications and/or integrity. , create and host links, and create and decode VBLS objects.

In embodiments, the CG-modules of the CG-ESP 400 include a root DNA module 410, an executable isolation component (EIC) module 420, a linking module 430, a sequence mapping module 440, a binary conversion module 450, an authentication module 460, and/or a master module. An integrity controller 470 may also be included. As noted above, in some embodiments, the CG-enabled digital ecosystem includes a set of VDAXs, whereby the set of VDAXs comprises two or more classes of VDAX (e.g., ecosystem VDAX(s) , enclave VDAX(s), cohort VDAX(s), and/or dependent VDAX(s)). In some of these embodiments, each class of VDAX includes CG-ESP modules (e.g., root DNA module 410, EIC module 420, linking module 430, sequence mapping module 440, binary conversion module 450, authentication module 460, and /or may run a respective instance of some or all of the master integrity controllers 470). In embodiments, individual modules may be cryptographic-based, non-cryptographic, and/or hybrid (eg, including functionality that is cryptographic-based and non-cryptographic).

In embodiments, each CG-ESP instance is executed by a respective processing system, which may include one or more CPUs, GPUs, microcontrollers, FPGAs, microprocessors, special purpose hardware, and/or the like. may be Additionally, in some embodiments, modules of a CG-ESP instance may be executed within by a virtual machine or container (eg, a Docker container).

In embodiments, CG-ESP 400 includes root DNA module 410 . In embodiments, the root DNA module 410 manages ecosystem-specific data and genomic processes from which the root DNA module 410 generates genomic constructs (e.g., DNA sets ). In some embodiments, root DNA module 410 may include CNA module 412, PNA module 414, LNA module 416, and/or XNA module 418.

In embodiments, the root DNA module 410 manages ecosystem specific data and CG genomic processes from which the root DNA module 410 forms CNAs that allow for specific and highly precise differences and correlations. In embodiments, CG-enabled ecosystem component eligibility-correlation is enabled by the CG genome process of formulating and building CNA objects. In embodiments, the CNA module 412 implements CG-genome processes and related methods configured to establish specific relationships between individual ecosystem components (ecosystems, enclaves, cohorts, and/or dependent cohorts). may be defined. In embodiments, a CNA may allow VDAXs in the same ecosystem to confirm eligibility for engagement. In embodiments, CNA enables ecosystem VDAX and sub-ecosystem VDAX to uniquely maintain eligibility confirmations for engagement. In embodiments, the CNA module 412 may be configured to infer genome-based eligibility correlations using a wide range of information-theoretically-enhanced computationally complex functions. In embodiments, these information theory-enhanced functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, the root DNA module 410 manages ecosystem-specific data and CG genomic processes from which the root DNA module 410 forms PNAs that enable specific and highly precise differences and correlations. In embodiments, CG-enabled ecosystem component eligibility synchronization is enabled by the CG genome process that formulates and builds PNA objects. In some embodiments, the PNA module 414 defines CG-processes that employ CG-genomic processes and associated computational methods to establish specific relationships between individual ecosystem components. In this way, the PNA may allow ecosystem components (eg, enclaves, cohorts, and/or dependent cohorts) of the same ecosystem to confirm their eligibility for engagement. In embodiments, the PNA allows an ecosystem VDAX and sub-ecosystems of descendant VDAXs to nevertheless retain unique confirmation of eligibility for engagement. In embodiments, the PNA root module 414 may be configured to invoke genome-based eligibility synchronization, which may be computed according to a wide range of information theory-driven computational complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

In embodiments, the Root DNA module 410 manages ecosystem specific data and CG genomic processes from which the Root DNA module 410 forms LNAs that allow for specific and highly rigorous differentiation and correlation. In some embodiments, CG ecosystem component link exchange-correlation is enabled by the CG genome process of formulating and building LNA objects. In embodiments, the LNA module 416 defines CG-processes and associated computational methods to establish specific relationships between individual ecosystem components. In this way, the LNA may allow specific VDAXs within the ecosystem (eg, members of the same enclave) to verify link exchange-correlation. In embodiments, the LNA enables VDAXs within the digital ecosystem to conduct information exchanges (“link exchanges”) that allow each to engage the other, whereby the link exchanges Bear the complexity. In some embodiments, CG-based LNA-enabled link exchanges are: It assumes two sets of information, each unique to one of the parties. In embodiments, the LNA root module 416 looks for genome-based link exchange-correlations, which can be computed according to a wide range of information-theoretically-driven computational complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, cryptographic-free, or hybrid hybrid computational complexity functions.

In embodiments, the Root DNA module 410 manages the ecosystem specific data and CG genome processes from which the Root DNA module 410 forms the specific and highly precise differences that make XNA possible. In these embodiments, engagement-differentiation of ecosystem members may be enabled by the CG genome process of formulating and building XNA objects. In embodiments, the root XNA module 418 employs XNA-specific CG-processes and associated computational methods to establish specific relationships between individual ecosystem components. In embodiments, XNA allows VDAX in the same ecosystem to identify engagement differentiation. In some embodiments, XNA allows different ecosystem VDAXs (eg, ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX) to confirm engagement-differentiation. Engagement-differentiation allows pairs of VDAXs to be sufficiently differentiated from other well-correlated VDAXs for the purpose of securely exchanging data, whereby engagement bears the corresponding computational complexity. do. In some embodiments, XNA-enabled engagements are each directed to one of the parties such that the engagement between two VDAXs (e.g., a first VDAX and a second VDAX) is unique (e.g., bisymmetric). Two unique sets of information can be assumed. In embodiments, the XNA module 418 charges genome-based engagement derivatives, which can be computed according to a wide range of information theory-enhanced computational complexity functions. In embodiments, the information-theoretically facilitated function may be a cryptographic-based, non-cryptographic, or hybrid computational complexity function.

In embodiments, the CG-ESP may contain ecosystem-specific data and an EIC module 420 that manages the CG-genome process, from which the EIC module 420 formulates specific and very strict differential realization structures called ZNAs. become In embodiments, ecosystem EIC engagement-differentiation is enabled by the CG-genome process of formulating and building ZNA objects. In embodiments, ZNA allows VDAX in the same ecosystem to directly control genome-enabled differentiation processes in which no other VDAX component participates. For example, in embodiments, the EIC VDAX (e.g., core and memory) may employ ZNA-specific genomic processes and other related computational methods to establish differentiation from other specific EIC VDAX. can. In embodiments, the EIC module 420 may define a CG-process for probing genome-based engagement differentiation, which may be computed according to a wide range of information theory-enhanced computational complexity functions. In embodiments, the information-theoretically facilitated function may be a cryptographic-based, non-cryptographic, or hybrid computational complexity function.

In embodiments, the link module 430 is a CG that allows two VDAXes (eg, a first VDAX and a second VDAX) to securely exchange information necessary to enable bisymmetric engagement. - Define a set of processes and related calculation methods. In some embodiments, link exchange exhibits the same level of entropy as biphasic engagement. In some embodiments, a link module 430 instance may be configured to verify engagement eligibility and link exchange correlation with another VDAX. In embodiments, engagement eligibility and link exchange correlations allow VDAX pairs to successfully exchange links (eg, spawn and host links). In embodiments, the linking module 430 may be configured to confirm engagement eligibility with another VDAX based on its genomic engagement object (eg, CNA or PNA). For example, the link module 430 may use its corresponding CNA object to confirm engagement correlation and/or its corresponding PNA object to confirm elicitable synchronization. In embodiments, a link module of a VDAX (eg, a first VDAX) may be configured to confirm a link exchange-correlation with another VDAX based on the genomic correlation objects of the first VDAX. In some embodiments, the linking module 430 instance links other VDAXs (e.g., LNA objects) to other VDAXes (e.g., second 2 VDAX). In embodiments, the link module 430 instance of the VDAX (e.g., the first VDAX) uses, in part, the genome correlation object of the first VDAX from links provided by other VDAXes or other Instead of VDAXes, links may be hosted by decoding information that engages with other VDAXes. Different configurations of link module 430 utilize various CG genome processes and associated computational methods to perform secure link exchanges across a wide range of interoperable digital communication media, digital networks, and/or digital devices. Note that it is possible to Note that link exchanges between VDAXs may be performed asynchronously, in that the order of exchange does not affect the security of the protocol. Further, in embodiments, the link exchange is one VDAX providing a link to another VDAX (e.g., symmetrical) or both VDAXs providing a link to each other VDAX (e.g., symmetrical). symmetrical). In an embodiment, the link module 430 may define a CG-process that drives the exchange of genome-based information, which may be computed according to a wide range of computationally complex functions driven by information theory. . In embodiments, the information-theoretically facilitated function may be a cryptographic-based, non-cryptographic, or hybrid computational complexity function.

As noted above, the link may contain information that enables biphasic engagement. In embodiments, the information included in the link may include genome regulation instructions (GRI). In some embodiments, GRI may define instructions and/or data used to modify genomic differentiation objects (e.g., XNA or ZNA) to modify in a deterministic manner. The instructions and/or data used may be defined such that if a first VDAX provides a link to a second VDAX and the second VDAX successfully decodes the GRI contained in the link, the first Both the VDAX and the second VDAX can modify their respective genomic differentiation targets using GRI, such that highly correlated copies of the modified genomic differentiation targets (eg, modified XNA or modified ZNA) are generated. As used herein, "highly correlated" as used in reference to genomic objects may refer to identical and/or other well-correlated genomic objects, whereby two or more Two genomic objects are said to be "sufficiently correlated" if the degree of correlation between the genomic objects allows the intended CG operation or process to be performed successfully. In embodiments, the GRI may contain additional information such as instructions and/or data used by VDAX during sequence mapping. As will be explained in more detail, such deterministic modifications allow two cohorts to be differentiated from all other cohorts to effect the generation of secure VBLS. In embodiments, link module 430 may generate a GRI for each link such that a unique GRI is generated for any respective engagement. In some embodiments, link module 430 may encode GRI using link-specific engagement factors to obtain encoded GRI. The link module 430 provides a genome engagement cargo (GEC) containing encoded GRI and additional information used by the link-hosting VDAX to decode the GRI from the GEC based on the information and the genome data of the link-hosting VDAX. may be generated. In an embodiment, the Linking Module 430 is further configured to decode links (which is part of "link hosting"), whereby the Linking Module 430 can access the information contained in the GEC and its genome datasets. and obtain the GRI by decoding the encoded GRI using the genomic coefficients. The decoded GRI can then be used by the link-hosting VDAX in generating the VBLS of the link-spawned VDAX that provided the link.

In embodiments, the link module 430 may be further configured to update the links. Link updating may refer to the process by which genomic regulatory instructions (GRIs) provided by a first VDAX to a second VDAX are modified for a specific engagement between a pair of VDAXs. Links may be updated for any number of reasons, including concerns that a link has been compromised, and/or according to routine security protocols (e.g., links may be updated daily, weekly, or monthly, or cohort requests to update links). (updated in response to In some embodiments, the link module 430 may update the link by generating link update information, whereby the link update information is updated from the VDAX that generated the link to the VDAX hosting the link. provided to In embodiments, the link update information may include a new GRI that replaces the current GRI. In other embodiments, link update information may include data used to modify the current GRI. For example, link update information is updated by the hosting VDAX applying values to the current GRI using one or more computationally complex functions (e.g., crypto-based, non-cryptographic, or hybrid functions). may be the value used to transform the GRI so as to obtain the GRI In some embodiments, link updates differ from link exchanges in that link update information can be encoded in VBLS, as opposed to link exchanges, which can involve more computationally expensive operations. Thus, link exchange may be performed as a one-time transaction and link update may be performed any number of times and/or for any suitable reason.

In embodiments, a link may be a static link or a dynamic link. In embodiments, dynamic links may refer to links that contain additional information that further differentiates cohort pairs. In some embodiments, dynamic linking may include executable code (or references to executable code) used to modify one or more of the functions performed by the set of VDAX, Only in terms of their engagement. For example, dynamic links may include executable code that modifies XNA/ZNA modification functions, sequence mapping functions, and/or binary conversion functions for each engagement. In this way, when a VDAX pair exchanges dynamic links, the cohort pair will use the default code when performing a particular function (e.g., XNA/ZNA differentiation, sequence mapping, and/or binary conversion). Executable code can be executed instead of or in addition to the default code. In embodiments, static links may refer to links used in engagements where the configuration of the CG-ESP does not change for a particular engagement.

In embodiments, the static linking module 432 ensures that two VDAXes (e.g., a first VDAX and a second VDAX) securely exchange information (e.g., spawn link and host link) and this exchange defines a CG process that exhibits the same level of entropy. In embodiments, the rules and processes governing static linking are defined by the highest class VDAX in the ecosystem (e.g., the ecosystem VDAX), whereby the rules apply to all link VDAXs in the ecosystem. can be In embodiments, the static link module 432 instance may perform CG-processes associated with CNAs used for eligibility-correlation and/or PNAs used for eligibility-synchronization. In some embodiments, the static link module 432 instance runs the CG-processes associated with the LNAs used for link exchange-correlation. In embodiments, a CG platform instance may be configured to execute processes to facilitate secure link exchanges across a wide range of interoperable digital communication media, digital networks, and/or digital devices. In embodiments, VDAX may perform link exchanges asynchronously, in that the order of exchanges does not affect the security of the protocol. Further, in embodiments, the link exchange may be one VDAX providing a link to another VDAX (e.g., symmetrical) or both VDAXs providing a link to each other VDAX (e.g., two VDAXes). symmetrical). In some embodiments, the link module 432 instance processes genome-based engagement differentiation, which can be computed according to a wide range of information theory-driven computational complexity functions. In embodiments, the information-theoretically facilitated function may be a cryptographic-based, non-cryptographic, or hybrid computational complexity function.

In an embodiment, the dynamics link module 434 provides information (eg, spawn link and host link ), thereby making the exchange exhibit the same level of entropy. In embodiments, the rules and processes governing dynamics linking are best-in-class VDAXs within the ecosystem (e.g., ecosystem VDAX), including the authority to establish additional genomically compatible link exchange orders and processes. Defined by

In an embodiment, the dynamic linking module 434 is a dynamic linking module containing executable instruction sets (e.g., binary code, scripts, etc.) that modify various CG-processes as permitted by the highest level VDAX of the CG-enabled ecosystem. A link may be generated. In these embodiments, the executable instruction set in dynamic linking can override the functionality of specific modules (e.g., XNA module, sequence mapping module and/or binary conversion module) for specific engagements. . In this way, VDAX pairs that have exchanged dynamic links can change their CG-processes performed for that particular engagement, which can provide an additional layer of security. In some embodiments, the dynamic linking module 434 dynamically links so that the set of instructions processed is executed for a particular engagement, overriding one or more CG-processes executed during that engagement. may include an interpreter or just-in-time compiler that processes the instruction set contained in In some embodiments, the first dynamic linking module 434 instance may generate dynamic links that include executable instruction sets. In these embodiments, the second dynamic link module 434 instance of the second VDAX is such that when the second VDAX is generating VBLS to the first VDAX, each dynamic link module 434 both A dynamic link may be decoded so that an override CG-process(es) can be used for that particular engagement. The second VDAX may use the override CG-process(es) to generate VBLS, while the first VDAX uses the override CG-process(es) to decode the VBLS can be used. Reverse data exchange using the second dynamic link from the second dynamic link module 434 instance to the first dynamic link module 434 instance is defined by the first VDAX on the second dynamic link. similarly in that the second VDAX uses the overriding CG-process(s) to generate a second VBLS and the second VDAX decodes the second VBLS using the overriding CG-process(s) It will be appreciated that it can work.

In embodiments, a dynamic link module 434 instance may establish processes for instructions and related CG-processes not shared by other VDAXs, which are governed by a wide range of options, situations, conditions, and objectives. there is a possibility. In embodiments, dynamic linking provides an additional level of security because the CG-process itself is modified in a unique way for a unique pair of VDAX.

In embodiments, dynamic link module 434 may perform dynamic link exchanges asynchronously, in that the order of exchanges does not affect the security of the protocol. Further, in embodiments, the link exchange performed by the dynamic link module 434 may be one VDAX providing a link to another VDAX (e.g., symmetric), or both VDAXs linking to each other VDAX. (eg, di-symmetric). In embodiments, dynamic link module 434 instances process genome-based engagement differentiation, which can be computed according to a wide range of information-theoretically driven computational complexity functions. In embodiments, the information theory-facilitated function may be a cryptographic-based, non-cryptographic, or hybrid computational complexity function.

In embodiments, the sequence mapping module 440 may define a set of CG-processes and computational methods that perform sequence mapping. In some embodiments, sequence mapping may be an important computation for transforming unique non-reproduction digital objects. In embodiments, a sequence mapping module 440 instance may (modify) public sequences (eg, public protocol and/or format dependent metadata) and/or private sequences (eg, private and proprietary protocol and/or format dependent metadata). It may be configured to map to genomic data objects. Sequences may be widely heterogeneous (e.g., TCP, UDP, TLS, HTTP, H.265, or other public or private sequences), whether they are public or private, and may be specific can be mapped to the modified genomic data to obtain results (eg, genome engagement factors) that show the level of entropy of . In embodiments, sequence mapping module 440 may include public sequence mapping module 442 and/or private sequence mapping module 444 .

In embodiments, the public sequence mapping module 442 may define CG-enabled processes and related methods configured to select specific sequences from public sources (eg, specific protocol- or format-dependent metadata). In some embodiments, a public sequence mapping module 442 instance may process a given public sequence (“PBS1”) to derive a particular value (“VI”) (e.g., hash function or using another computationally complex function). In an embodiment, the resulting value VI is then processed (eg, mapped) according to a genomic differentiation object (eg, XNA1) associated with the published sequence to obtain a specific entropy (eg, genomic engagement factor) A unique vector is generated that indicates In embodiments, the value VI may be processed (eg, mapped) according to an alternative genomic differentiation object (eg, XNA2) to generate different unique vectors indicative of a particular entropy. In embodiments, the resulting vector can exhibit a level of entropy that greatly exceeds the size of the published sequence used to derive the vector. In embodiments, public sequence mapping module 442 generates unique vectors that can take advantage of specific facilities that exist in unrelated protocols and formats. In an embodiment, published sequence mapping module 442 performs genome processing computed according to information theory-driven composite functions to generate unique vectors based on published sequences and genomic differentiation objects (e.g., modified XNA objects). to run. In embodiments, these information theory-facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, the private sequence mapping module is configured to select specific sequences from a private source (e.g., private and/or proprietary protocol or format dependent metadata) and derive a unique vector indicative of a specific entropy. may define a CG-enabled process and related methods that In some embodiments, a private sequence mapping module 444 instance may process a given private sequence (“PVS1”) to derive a particular value (“VI”). In an embodiment, the resulting value VI is in turn processed (eg, "mapped") according to the genome differentiation object (eg, XNA1) associated with the private sequence module 444 to create a unique vector indicating a particular entropy. to generate In embodiments, the value VI may be processed (eg, mapped) according to an alternative genomic differentiation object (eg, XNA2) to generate different unique vectors indicative of a particular entropy. In embodiments, the resulting vector may exhibit a level of entropy that greatly exceeds that of the private sequence used to derive the vector. In embodiments, private sequence mapping module 444 instances generate unique vectors that can take advantage of specific facilities that exist in unrelated private protocols and formats. In an embodiment, the private sequence mapping module 444 instance follows a set of information-theoretically-enhanced computationally complex functions to generate unique vectors based on private sequences and genomic differentiation objects (e.g., modified XNA objects). Run the computational genome process. As used herein, the term "set of information-theoretically accelerated computational complexity functions" can refer to any combination of one or more information-theoretically accelerated computational complexity functions. In embodiments, these information theory promoted functions are cryptographic-based functions, cryptographic-free functions, or at least one stage that leverages cryptographic-based functions and at least one stage that leverages cryptographic-free functions. It may be a hybrid computational complexity function comprising

In embodiments, the binary translation module 450 may define a set of CG-processes and associated computational methods configured to generate a virtual binary (eg, object-to-object) language script (VBLS). In embodiments, a binary conversion module 450 instance translates digital objects (e.g., packets, sectors, sequences, and/or frames) having a particular format and protocol into various computational methods (e.g., disambiguation methods and/or cryptographic methods). conversion method). In embodiments, a binary conversion module 450 instance encodes a digital object based on values (e.g., genomic engagement factors) determined by corresponding sequence mapping modules 440 to provide unique, non-recursive, and/or computational is configured to generate an encoded digital object that can be a quantum proof of In embodiments, the binary conversion module 450 instance may be further configured to decode encoded digital objects using values (eg, genomic engagement factors) determined by the corresponding sequence mapping module 440 . In embodiments, binary conversion module 450 may include disambiguation module 452 and/or encryption module 454 .

In embodiments, the disambiguation module 452 defines CG-processes and computational methods that perform binary transformations of digital objects according to the genomically derived genomic engagement factors generated by the corresponding sequence mapping module 440 instance. well, whereby the resulting encoded digital object is only subject to brute force attacks. In embodiments, the disambiguation module 452 instance may transform the digital object based on the genomic engagement factor by performing an XOR operation on the genomic engagement factor and the digital object to obtain an encoded digital object. . In embodiments, the disambiguation module 452 instance uses each It can be configured to receive different genomic engagement factors for the digital object. In embodiments, the disambiguation module 452 instance may be configured to decode the encoded digital object using the inverse disambiguation function and the genomic engagement coefficients. Assuming that the genomic engagement factors match the genomic engagement factors used to encode the digital object, the inverse disambiguation function computes the decoded digital Output an object. In an embodiment, the disambiguation module 452 instance performs genome processing according to an information theory-driven composite function. In embodiments, these information theory-enhanced functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, the encryption module 454 may define CG-processes and computational methods that perform binary transformations of digital objects according to genome-derived genome engagement factors generated by corresponding sequence mapping module 440 instances, which , the resulting encoded digital object is only subject to brute force attacks. In an embodiment, a cryptographic module 454 instance receives a genomic engagement factor and a digital object as input, and uses any suitable cryptographic function to output an encoded digital object, based on the genomic engagement factor. Objects can be transformed. In embodiments, the encryption function used must have a corresponding de-encryption function (or decryption function) that can be used to decrypt the encoded digital object. In embodiments, a cryptographic module 454 instance may be configured to receive different genomic coefficients for each digital object, or may use the same transform for two or more different digital objects. .

In embodiments, the encryption module 454 instance may be configured to decrypt the encoded digital object using the inverse encryption function and the genome engagement coefficient. Assuming that the genomic engagement factor matches the genomic engagement factor used to encrypt the digital object, the inverse encryption function can find the decrypted digital object given the genomic engagement factor and the encoded digital object. to output In embodiments, cryptographic module 454 instances perform genome processing according to information-theoretically-enhanced computationally complex functions. In embodiments, these information theory-enhanced functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, the validation module 460 may define CG-processes and computational methods configured to validate VDAXs that have a common genomic organization. As explained, digital ecosystems built by top-level VDAX (e.g., ecosystem VDAX) have specific distributions of genomic data (e.g., CNA, PNA, LNA, XNA, and/or ZNA) and , specific genome eligibility-correlation, eligibility-synchronous link exchange-correlation, and/or engagement-correlation attributes. In embodiments, authentication module 460 instances have a common construction (e.g., associated genomic data) regardless of their primary genomic construction (e.g., members of different enclaves within the digital ecosystem) for which the corresponding VDAX is Any other VDAX engagement correlation may be configured to be verified. In embodiments, the authentication module 460 confirms the engagement correlation of the corresponding VDAX with another VDAX of the same CG-enabled digital ecosystem based on their common genome construction, regardless of which enclave the VDAX belongs to. It may also include an inter-cohort module 462 that defines the CG-processes and associated computational methods that enable it. In an embodiment, an authentication module 460 instance is configured to invoke secure genome-based engagement correlations of genomic datasets according to information-theoretically-driven computationally complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

As explained, the suitability of root DNA constructs and supporting genomic processes (eg, link generation, engagement correlation, VBLS generation, etc.) are directly managed and controlled by the specific organization of CG-modules. In embodiments, each CG-ESP may include the CG-processes and associated calculation methods of the master integrity controller 470 that manages module compatibility on behalf of the VDAX. In embodiments, the master integrity controller 470 may include CG-processes and associated computational methods that ensure operational performance and configuration management truthfulness for VDAX across the digital ecosystem. In embodiments, master integrity controller 470 may include genomic process controller 472 , authorization module 474 , and engagement instance module 476 .

In embodiments, engagement of VDAXs, their genomic modules, and other such functional modules is performed by each master integrity controller 470 of each CG-ESP instance (eg, may be performed by the corresponding VDAX). may be controlled by an instance. In embodiments, the master integrity controller 470 instance leverages computationally complex functions to engage with specific modules (eg, lto N). In some of these embodiments, the master integrity controller 470 instance has respective genomic datasets (e.g., CNA, PNA, LNA, and/or XNA) as well as modules and engages with specific modules. Computationally complex functions may be used to manage. In some embodiments, the genomic process controller 472 may verify module integrity and authenticate modules using their genomic data and computational complexity functions. In these embodiments, the genomic process controller 472 instance is not configured to determine the processes and functions performed by each VDAX. In order to safeguard the computationally complex genomic processes carried out by each VDAX, the genomic process controller 472 may control the operational processes and functions associated with the correct application of modular processes and functions; and to the correct application of functions specific operational processes and functions may be rendered under the control of specific modules. Stated another way, in embodiments, the genomic process controller 472 may verify the source of the CG-ESP module instance and/or verify or deny the integrity of the CG-ESP instance, and the module It may be any process and operation performed in support of an instance (eg, the process of connecting modules to various CG-based functions).

In embodiments, the VDAX, which utilizes the same computationally complex genomic functions as the modules and master integrity controller 470, can validate or disqualify a particular CG-ESP configuration. For example, in embodiments, a VDAX (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, and/or a dependent VDAX) that utilizes the same computationally complex genomic functions as modules and master integrity controllers 470 is configured with a specific CG-ESP Configurations can be confirmed or disqualified. In embodiments, the VDAX, which utilizes the same computationally complex genomic functions as the modules and master integrity controller 470, is capable of validating, disqualifying, or altering specific CG-ESP configurations. In embodiments, master integrity controller 470 may include genomic process controller 472 , authentication module 474 , and engagement instance module 476 . In an embodiment, the genomic process controller module 472 instance institutes secure genome-based verification, disqualification, and modification of VDAX modules and specific VDAX configurations, which are extensive information-theoretically facilitated computationally complex It can be calculated according to a function. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX) are heavily adopted, deployed, and implemented in that configuration control can be influenced horizontally and/or hierarchically. and provide operational flexibility. In embodiments, this flexibility derives from the same inherent computationally complex genomic functions (eg, correlation and differentiation) facilitated by the CG-ESP module. In embodiments, VDAXs (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAXs) are organized so that a single ecosystem or enclave VDAX (e.g., descendants) can determine the operational configuration of other VDAXes. Alternatively, it may be independently configured and enabled (eg, master integrity controller 470 intercommunication). In embodiments, a descendant (eg, ecosystem VDAX or enclave VDAX) can directly confirm or disqualify the operational status of other VDAXes based on the other VDAX's configuration. In embodiments, an ancestor (e.g., an ecosystem VDAX or an enclave VDAX) possesses unique genomic properties that are configured and valid such that authorized module updates of VDAX can be performed in coordination with other authorized CG-ESP modules. You may In embodiments, the master authorization module 474 instance performs secure genome-based verification, disqualification, and modification of VDAX modules and specific VDAX configurations that may be computed according to a wide range of information-theoretically driven computational complex functions. carry out In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, engagement between two or more VDAXs (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX) using computationally complex genomic features enabled by EG-CSP is performed in a single configure a security instance for In some embodiments, these security-instances may be aggregated according to the hierarchical genomic relationships exhibited by a particular digital ecosystem community. In embodiments, security-instances aggregated at a lower level are passed to the next or any other higher aggregation point (e.g. cohort VDAX to enclave VDAX) and so on (e.g. cohort VDAX and enclave VDAX to ecosystem VDAX). In embodiments, communication between VDAX modules (eg, security-instance reporting) may be based on the same or different computationally complex genomic functions over which their primary security-instances are managed. In embodiments, the master engagement instance module 476 instance enables VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) to track security-instances according to a set of engagement tracking policies. do. In some embodiments, these policies may define how security-instances are defined. In embodiments, these definitions may impose certain computationally complex security functions. In an embodiment, the master engagement instance module 476 instance controls the VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) that a number of security-instances are created according to the engagement accounting policy. make it possible to calculate In embodiments, these policies define how security instances are accumulated. In embodiments, such storage may bear certain computationally complex security functions. In embodiments, a master engagement instance module 476 instance is a VDAX that has a common structure (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) provides security to other VDAXes according to a set of engagement reporting policies. Allows instances to be reported. In embodiments, these policies define how security-instances are reported, how often, and to whom. In embodiments, such reporting bears certain computationally complex security features. In some embodiments, VDAXs (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) are connected to each other by a single VDAX (e.g., ecosystem VDAX) to a digital ecosystem (e.g., community) engagement. It may be uniquely configured and enabled to define tracking policies, engagement accounting policies, and/or engagement reporting policies. In some embodiments, a single engagement instance module 476 instance may implement multiple tracking, accounting, and/or reporting policies using a particular computationally complex genomic function.

In embodiments, the Master Engagement Instance module is configured such that VDAXes with a common structure (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) are connected to other VDAXs (e.g., ecosystem VDAX, enclave VDAX, security instances from cohort VDAX, and/or dependent VDAX) can be aggregated. To be able to aggregate security instances from other VDAXs (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX) enabled by specific computationally complex genomic features, according to engagement reporting policy , providing ecosystem VDAX. In embodiments, the Master Engagement Instance module 474 instance performs secure genome-based tracking, accounting, reporting, and aggregation of VDAX genome-specific security instances that can be computed according to a wide range of information theory-driven cryptographic computational complexity functions. to prosecute. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

It is understood that FIG. 4 is provided for illustration. Additional or alternative modules may be used to configure the CG-ESP without departing from the scope of this disclosure. As explained, different CG-ESPs may be configured to perform different CG-operations on different configurations of genomic datasets. Examples of genomic datasets and different CG-manipulations performed on genomic data are described in more detail below.

FIG. 5 shows an exemplary embodiment of a genomic data set 300 (also called "digital DNA set", "DNA set" or "DNA"). As discussed, in embodiments a CG-ESP (eg, CG-ESP400) is constructed using a set of CG-processes and associated computational methods that operate on a particular genomic dataset. FIG. 3 shows examples of different types of genomic data that can be implemented for different CG-ESPs. It is understood that other types of genomic data may be developed later.

In an embodiment, the DNA set 300 used in connection with CG-ESP consists of one or more different types of digitally generated mathematical objects exhibiting composable entropy (these instances are commonly referred to as "genomic data"). or "DNA objects"). In some embodiments, the digitally generated mathematical objects of the DNA set may include any suitable combination of genome qualification objects 310, genome correlation objects 320, and/or genome differentiation objects 330. As will be explained, each different implementation of CG-ESP will have different combinations, types, and sizes can be utilized and supported. Examples of different goals can include performance and efficiency goals, security goals, resource allocation goals (eg, memory, storage, processing power, network bandwidth, etc.), economic goals, and the like. Moreover, certain ecosystems have different constraints or advantages. For example, certain controlled ecosystems (e.g., some executable ecosystems) have very limited specific cohorts (e.g., dependent cohorts of applications, sensors, device drivers, processors, memory devices, etc.) It may only be necessary to establish numerical relationships (eg, via links). In these scenarios, links for each relationship within the ecosystem may be created at the time the ecosystem is created such that each VDAX has access to any and all links required. . In such scenarios, a DNA set may not derive any additional benefit from having a particular type of DNA object, and a DNA set for such an ecosystem may be a genome eligibility object 310 or It may be configured without the genome correlation object 320, but may include the genome differentiation object 330. In another exemplary scenario, implementations of engagement eligibility determination, link exchange, and/or differentiation/VBLS generation are performed using a single DNA object (e.g., engagement eligibility verification, link exchange, and VBLS (via a unique crossover of each DNA object of each pair of VDAXs used in the determination). In this example, community owners may wish to sacrifice additional security measures to reduce storage requirements associated with storing different types of genomic data objects. In other scenarios, the community owner can control the amount of entropy shown for each type of DNA object in the DNA set based on the type of data structure and/or size of the data structure selected. For example, a genomic differential object 330 implemented as a 512×512 bit binary vector or bit matrix can provide quantum proof level security.

In an embodiment of the present disclosure, the genomic eligibility object 310 is a digital eligibility object that allows a pair of cohorts to confirm matchmaking eligibility, which can be performed as part of an "untrusted" authentication process between two cohorts. may refer to a mathematical object generated by In embodiments, an ancestral VDAX (e.g., an ecosystem VDAX or an enclave VDAX), based on its genome eligibility object (an "ancestral genome eligibility object"), determines the number of progeny VDAXes that will participate in the respective digital ecosystem. A progeny genome eligibility object 310 may be derived for. In these embodiments, each progeny VDAX may receive a derivative of a unique but correlated progenitor genome eligibility object. Additionally, in some implementations, all genome eligibility objects of an ecosystem may be derived from an ancestral genome eligibility object, and any member of the ecosystem may be linked to a correlated genome eligibility object (e.g., an ancestral genome eligibility object). intersections or shared parts of eligibility objects), any relationships with other ecosystem members can be ascertained. Once assigned a genomic eligibility object for a particular community, progeny VDAX may receive that genomic eligibility object. In some embodiments, a progeny VDAX is added to its genome data set via a one-time trusted event (e.g., upon entry into a particular enclave, upon manufacture, configuration, or sale of a device, etc.). A genome eligibility object can be received. After receiving each genome correlation object 310, the VDAX uses each genome eligibility object 310 to independently confirm engagement eligibility with one other VDAX in its enclave and/or ecosystem. be able to. In embodiments, the genomic correlation object 310 for a particular CG-enabled digital ecosystem includes a CNA object 312, a PNA object 314, and/or two community members confirming engagement eligibility and/or engagement completeness. It may be selected from other suitable mathematical constructs that allow.

In embodiments, CNA may refer to a genomic mathematical organization that allows a VDAX to uniquely determine that another VDAX is part of the same ecosystem community. In embodiments, this ecosystem correlation may be computationally quantum proven. In embodiments, ecosystem correlations performed by VDAX are based on common computationally complex genomic functions and may be performed without any form of consultation with a central authority (eg, a trusted third party). These correlated attributes indicate that two VDAXs in the same ecosystem that were activated years apart could have their ecosystems without prior knowledge of the other and without any consultation with a trusted third party. allows you to check the status of

In embodiments, CNA objects 312 may be implemented as binary vectors, binary matrices, and the like. In embodiments, the CNA object 312 is configured to exhibit a certain entropy. In some embodiments, the ecosystem's CNA entropy is a controllable entropy whereby the entropy may be configured by, for example, community owners. In some implementations, the configurable level of entropy of CNA object 312 may be a substantially anti-quantum level of entropy. For example, a substantially quantum-proof CNA object may be configured to exhibit a level of entropy of 256 bits or more. For example, in some embodiments such a level of entropy may be achieved by a CNA object implemented as a 512×512 bit binary vector or matrix. It is understood that quantum-proof CNA objects 312 may exhibit less entropy in some example implementations. It is understood that CNA objects 312 that exhibit less entropy may be used (eg, determined by community owners or any other party that makes up the security platform). For example, community owners may wish to comply with jurisdictional regulations, and may therefore use CNA objects (or other genomic datasets) that exhibit lower levels of entropy, which is an overall security consideration. but requires less storage and processing demands. In embodiments, CNA312 may be configured to use a series of genomic processes and associated computational methods to establish specific relationships between individual ecosystem members and confirm their eligibility for engagement. .

In embodiments, CNA generation for genomic eligibility correlation applications results in large sets of random data that can be organized as specific binary vectors. In some embodiments, CNA generation for genomic eligibility correlation applications may be enabled by high-quality random processes with controllable entropy. In embodiments, CNA generation for genome-visible-correlation applications may be enabled based on specific mathematical foundations with controllable entropy. In embodiments, CNAs may be generated according to a wide range of information theory-facilitated complex functions. In embodiments, these information theory-enhanced functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions. Exemplary techniques for generating CNA objects, modifying CNA objects, and confirming eligibility for engagement are discussed in greater detail throughout this disclosure.

In embodiments, PNA may refer to a digital that allows a VDAX to uniquely determine that another VDAX is part of the same ecosystem community. In embodiments, this ecosystem correlation can be computationally quantum proven. In embodiments, the ecosystem correlations that VDAX performs are based on common computationally complex genomic functions and may be performed without any form of consultation with a central authority (eg, a trusted third party). These correlated attributes indicate that two VDAXs in the same ecosystem that were activated years apart could have their ecosystems without prior knowledge of the other and without any consultation with a trusted third party. allows you to check the status of

In embodiments, PNA object 314 may be implemented as a set of binary primitive polynomials, or the like. In embodiments, PNA objects 314 are configured to exhibit a particular entropy. In some embodiments, the entropy of the PNA objects 314 of the ecosystem is controllable entropy, whereby the entropy may be configured by, for example, community owners. In some implementations, the configurable level of entropy of PNA object 314 may be a substantially anti-quantum level of entropy. For example, substantially quantum-proof PNA object 314 may be configured to exhibit a level of entropy of 256 bits or more. For example, in some embodiments, such levels of entropy are divided into two different sets (e.g., a first vector representing a binary sequence of 2048 by 2048 bits and 216 randomly chosen may be achieved by a PNA object implemented as a second vector) representing a set of binary primitive polynomials. It is understood that quantum-proof PNA object 314 may exhibit less entropy in some example implementations. It is understood that PNA objects 314 with less entropy may be used (eg, determined by the community owner or any other party that configures the security platform). For example, community owners may wish to comply with jurisdictional regulations, and thus PNA objects exhibiting lower levels of entropy, requiring less storage and processing demands at the expense of overall security. (or other genomic datasets) may be used. In embodiments, the PNA may be configured to use a series of genomic processes and associated computational methods to establish specific relationships between individual ecosystem members and confirm their eligibility for engagement.

In embodiments, PNA generation for genomic eligibility correlation applications results in large sets of random data that can be organized as specific binary vectors. In some embodiments, PNA generation for genome qualification-correlation applications can be enabled by high-quality random processes with controllable entropy. In embodiments, PNA generation for genome erigation-erigation-synchronization applications may be enabled on certain mathematical grounds with controllable entropy. Exemplary techniques for creating PNA objects, modifying PNA objects, and confirming eligibility for engagement are described in greater detail throughout this disclosure. Exemplary techniques for generating PNA objects, modifying PNA objects, and confirming eligibility for engagement are discussed in greater detail throughout this disclosure.

In embodiments, genomic correlation objects 320 may refer to digitally generated mathematical objects that allow VDAX to establish correlations with each other. In embodiments, the genomic correlation object enables link exchanges between VDAXs, whereby a first VDAX is provided to a second VDAX to generate a link (also called a "link") hosted by it. and thereby the link provides instructions that the second VDAX uses to generate VBLS that only the first cohort can decode (assuming the link is held securely by the second VDAX) You may In embodiments, the genomic correlation object 310 is used in CG-ESP to ascertain link exchange correlations, whereby two ecosystem components (e.g., enclave VDAX, cohort VDAX, etc.) establish a specific relationship. so that they can engage with each other.

In an exemplary implementation of CG-ESP, the community member's genomic correlation object 320 of the digital ecosystem is implemented as an LNA object 322 . In embodiments, the LNA forms the basis for the VDAX to establish correlations with each other. The entropy exhibited by the LNA object 312 is important in terms of correlation quality. Non-recurrent correlated attributes that can be derived from certain computationally complex genomic functions. In some implementations of CG-ESP, LNAs can be generated (eg, by ecosystem VDAX) for genomic correlation applications on large sets of random data. In some embodiments, LNA object 322 is implemented as a binary vector, bit matrix, or other suitable structure. In embodiments, the LNA object 322 is configured to exhibit configurable entropy such that the level of entropy exhibited by the LNA object can be a factor in the overall degree of correlation. In embodiments, LNA generation may be performed by a high quality random process with controllable entropy. In some embodiments, LNA generation for genome correlation applications may be enabled based on certain mathematical grounds with controllable entropy. In embodiments, the LNA may be generated according to a wide range of information-theoretically-enhanced complex functions. In embodiments, these information theory-facilitated functions may be cypher-based, cypherless, or hybrid computational complexity functions.

In embodiments, a pair of VDAXs may engage in bisymmetric and/or unidirectional link exchanges based on their common LNA (e.g., both VDAXs have their respective LNAs assigned from the same ancestor). can. In embodiments, the first VDAX may modify its LNA object and the genome adjustment instructions based on the modified LNA such that the second cohort is the only other VDAX capable of decoding the mapped GRI. ("GRI") may be encoded. In embodiments, a GRI may include instructions indicating data (eg, one or more values) and how the data is used to distinguish VDAX pairs for data exchange. In embodiments, the GRI is a differentiation value (e.g., binary (embodied as a vector) may be used to modify the differentiation object. In some embodiments, the GRI may contain sequence correction values used during the sequence mapping process. In these embodiments, the sequence mapping process may be used as an input parameter to an information theory-facilitated computational complexity function that modifies the sequence to an intermediate value based on the derivative value, the intermediate value and the modified derivative object may be used as input to an information theory-facilitated computational complexity function that outputs a genome engagement value corresponding to the original sequence.

It is understood that encoding genome regulation instructions based on modified LNAs may involve intermediate operations. For example, in some implementations of CG-ESP, VDAX determines mapping sequences, uses computationally complex functions to map the mapping sequences to modified LNAs, and encodes GRI based on genomic engagement factors. may be configured to In these example implementations, a VDAX may provide a link to another VDAX, allowing the other VDAX to successfully decode the encoded GRI if the other VDAX has a highly correlated LNA. can. In some implementations of CG-ESP, VDAX LNA objects can be highly correlated if they are identical or otherwise well-correlated. In some embodiments, a link exchange is performed only once between a pair of cohorts unless one of the cohorts explicitly updates its respective link to modify the GRI. It is a limited process. Outside of such operation, a pair of cohorts can be used in some scenarios where each VDAX's LNA object is mutated (e.g., persistently modified) by, e.g., an ancestor VDAX or at its direction, after a successful link exchange. can continue to exchange data based on the respective links each of the cohorts generate. Examples of genome manipulation involving LNA objects 322 are discussed in greater detail throughout this disclosure, including techniques for generating LNA objects 322, techniques for modifying LNA objects 322, and techniques for performing link exchanges using LNA objects 322. discussed.

In an embodiment of the present disclosure, the genomic differentiation object 330 includes VBLS generated by a community member pair (e.g., cohort) if the community member pair successfully exchanged links and had sufficiently correlated genomic differentiation objects. It may refer to a digitally generated mathematical object that allows it to be exchanged and decoded. In some embodiments, the first VDAX modifies its genome differentiation object 330 in a manner defined in a genome regulation instruction (GRI) provided to the first VDAX in a link from the second VDAX. generates the VBLS of the second VDAX partially by and partially decodes the VBLS from the second cohort by modifying its genomic differentiation object 330 according to the GRI provided to the second cohort. In the CG-ESP embodiment, the first VDAX uses a computational complexity function (e.g., cryptographic-based, non-cryptographic, or hybrid computational complexity function) to generate a sequence (e.g., private or public sequence). Mapping to modified XNA objects yields genomic engagement factors that may be used to encode digital objects. Examples of genomic identification objects 330 can include, but are not limited to, XNA332 and ZNA334 objects.

In an exemplary implementation, the digital ecosystem community member genomic difference object 330 is XNA. In some embodiments, XNA is the core competence upon which all genomic differences are based. In these embodiments, XNA is based on a bisymmetric language (eg, VBLS) that VDAX employs to control unique non-regenerative engagement. In some embodiments, inherent non-repeating engagements can be quantum tolerant. In embodiments, the entropy exhibited by XNA may be important from a VBLS security point of view, with higher entropy providing a higher level of security. In embodiments, recursive difference attributes are derived from specific computationally complex genomic functions. In embodiments, XNA generation for genome differentiation applications results in large sets of random data that can be organized as specific binary vectors. In embodiments, XNA generation for genomic differentiation applications can be performed by high-quality random processes with controllable entropy. In some embodiments, XNA generation for genomic differentiation applications may be enabled on a specific mathematical basis with controllable entropy. In embodiments, XNA may be generated according to a wide range of information-theoretically driven complex functions. In embodiments, these information theory-enhanced functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In some embodiments, XNA objects 332 may be implemented as binary vectors, matrices, etc. that exhibit configurable entropy. In some embodiments, the entropy indicated by XNA object 332 determines the security of VBLS generated by community members. In embodiments, the XNAs assigned from the ancestral VDAX to each community member (eg, enclave member) are identical and/or otherwise sufficiently correlated. In some embodiments, a first VDAX that generates a VBLS for a second VDAX modifies its XNA object 332 according to the GRI that the second VDAX provides on the link. The first VDAX may then map sequences determinable by the second VDAX (eg, published or unpublished sequences) to the modified XNA objects 332 to obtain genomic engagement factors. Digital objects (e.g., processor instructions, packet payloads, disk sectors, etc.) are then encoded using cryptographic-based encryption or disambiguation and genomic engagement factors to obtain encoded digital objects contained in VBLS objects. may In embodiments, the VBLS object may further include metadata such as the sequences used to generate the genome engagement factor. The encoded digital objects of the VBLS results may then be provided to a second cohort. In these exemplary implementations, the second cohort receives the VBLS object and modifies its XNA332 according to the GRI contained in the link provided by (or on behalf of) the first VDAX to the second VDAX; Recreate genomic engagement factors by mapping the sequences to modified XNA. The genomic engagement factor may then be used to decode and obtain the encoded digital object using cryptographic-based decryption or disambiguation used to encode the digital object. . In these exemplary implementations, VDAX's ability to both modify each XNA object 332 using the same GRI and to determine genomic engagement factors in a deterministic manner ensures that the first cohort is a data object to a second VDAX, potentially varying the genomic engagement factor for each instance of data exchange (e.g., every packet, every sector, every shard, every frame, etc.). be. In this way, VBLS has quantum proof level security. Note that the preceding description is an example of how XNA or other genomic differentiation objects can be leveraged in a secure data exchange process.

In some embodiments, revocation of community members (e.g., cohorts) from a community (e.g., enclave) is accomplished by selectively mutating the XNA objects of some community members within the community by progeny VDAX. obtain. Note that "mutating" an XNA object may refer to instructing a descendant VDAX to permanently modify that XNA object or to providing a new XNA object to a descendant VDAX. In this way the mutated XNA is used for subsequent VBLS coding and encoding for a particular community. For example, in some exemplary implementations, the ecosystem VDAX can mutate the XNA of only the cohort that remains in the enclave. In this way, cohorts that have been revoked from the enclave can still engage with them, but cannot generate VBLS for or decode VBLS from cohorts with mutated XNA objects. If a community owner (e.g., a network administrator associated with an ecosystem and/or an ecosystem enclave) chooses to revive a cohort, the enclave VDAX will mutate the cohort's XNA so that the XNA was previously mutated. XNA that is sufficiently correlated with other community members in the enclave so that the cohort can then initiate data exchange with other cohorts in the enclave using previously established and/or future established links. .

In an exemplary implementation, the digital ecosystem community member's genomic difference object 330 is a ZNA. In some embodiments, ZNAs are the core competency upon which all viable segregating component genomic differences rely. In these embodiments, the ZNA forms the basis upon which a unique, non-recurring (potentially quantum proof) executable binary is controlled. EIC iterative transforms may be derived from certain computationally complex genomic functions. In embodiments, ZNA generation for genomic differentiation applications results in large sets of random data that can be organized as specific binary vectors. In embodiments, ZNA generation for genomic differentiation applications may be performed by a high quality random process with controllable entropy. In some embodiments, ZNA generation for genomic differentiation applications may be enabled on certain mathematical grounds with controllable entropy. In embodiments, ZNAs may be generated according to a wide range of information theory driven complex functions. In embodiments, these information theory-enhanced functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In some embodiments, ZNA object 334 may be implemented as a binary vector, matrix, etc., whereby ZNA object 334 exhibits configurable entropy. In some embodiments, ZNA may be structurally similar to XNA, but is used in a viable ecosystem. In embodiments, ZNA may be used to generate VBLS that are exchanged between executable ecosystem components. In some embodiments, the entropy indicated by ZNA object 334 determines the safety of VBLS generated by community members. In embodiments, the ZNAs assigned to each community member (eg, device component) from an ancestral VDAX are identical and/or otherwise sufficiently correlated. In some embodiments, a first VDAX (e.g., first EIC) that generates VBLS for a second VDAX (e.g., second EIC) uses the GRI that the second VDAX provides on the link. Modify that ZNA object 334 according to. The first VDAX can then map sequences determinable by the second VDAX (eg, published or unpublished sequences) to the modified ZNA objects 334 to obtain genomic engagement factors. A digital object (eg, processor instructions, disk sectors, etc.) may then be encoded using the complex function and the genomic engagement coefficients to obtain the encoded digital object contained in the VBLS object. In embodiments, the VBLS object may further include metadata such as the sequences used to generate the genome engagement factor. The VBLS resulting encoded digital object may then be provided to a second VDAX. In these exemplary implementations, the second VDAX receives the VBLS object, modifies its ZNA object 334 according to the GRI contained in the link provided to the first VDAX on behalf of the second VDAX, and converts the sequence to Recreate the genomic engagement factor by mapping to the modified ZNA object 334. The genomic engagement factor may then be used to decode and obtain the encoded digital object using the inverse of the bidirectional function used to encode the digital object. In these exemplary implementations, both the ability of VDAX to modify each ZNA object 334 using the same GRI and to determine the genomic engagement factors in a deterministic manner allowed the first cohort to divide the data objects into Can be securely provided to a second VDAX, potentially varying the genomic engagement factor for each instance of data exchange (e.g., every packet, every sector, every shard, every frame, etc.) . In this way, VBLS can provide quantum proof level security. Note that the above description is an example of how ZNAs or other genomic identification objects can be leveraged in a secure data exchange process.

As can be appreciated from the present disclosure, core genomic competencies (e.g., differentiation and correlation that support the CG-ESP process) are associated with production (e.g., LNA production, XNA production, ZNA production, CNA production, and/or PNA production). DNA generation, which may include), modifications (e.g., DNA modifications which may include LNA modifications, XNA modifications, ZNA modifications, CNA modifications, and/or PNA modifications), and genome-specific (e.g., LNA, XNA, ZNA, CNA, and/or digital DNA) allocations (eg, DNA allocations that may include LNA allocations, XNA allocations, ZNA allocations, CNA allocations, and/or PNA allocations). In embodiments, these application-specific DNA constructs (e.g., some combination of LNA, XNA, CNA, PNA, and/or ZNA) have specific transformations, important for controllable virtualization of differentiation. is.

In embodiments, ecosystem ancestors (eg, ecosystem VDAX) may mutate (eg, persistently modify) the genomic data 300 of some or all ecosystem members. In embodiments, mutation of genomic data 300 may refer to persistent modifications or updates of genomic data objects. For example, in an embodiment, the ecosystem may use the LNA objects 322, XNA objects 332, CNA objects 312 of some or all members of the ecosystem such that VDAX uses the mutated genomic data instead of the previous genomic data. , and/or may mutate the PNA object 314 . that the term "mutation" may be used to refer to modifications to the DNA object 300 that are persistent, as opposed to modifications during link exchange or VBLS generation, which may be transient modifications; Please note. However, it should be noted that modifications and mutations can have similar effects on DNA constructs, and where the context so suggests, the term "modification" can be used in connection with persistent modifications. Please note.

In embodiments, a community member's LNA object may be modified (eg, for link exchange) and mutated (eg, persistently modified/updated). ) As explained, a non-iterative correlation object (e.g., LNA) may be derived from a particular computationally complex genomic function, the correlation consisting of VDAX with various enclave relations N×M, It may contain a digital ecosystem with dimensions N×M. Such digital ecosystem relationships may need to change their correlation attributes to prevent the establishment of future or additional ecosystem relationships. LNA mutations allow specific (broad and narrow) redefinition of correlation attributes. In embodiments, the LNA genome structure can be tailored to a particular digital ecosystem organization and the structure is modifiable. In some embodiments, LNA random vectors can be modified uniformly or inconspicuously (broad and narrow) based on specific instructions. The LNA modifications maintain the genomic integrity of the LNA constructs and their correlated attributes. In embodiments, VDAX possessing modified LNAs cannot affect future correlations with VDAX possessing unmodified LNAs. In embodiments, LNAs may be genetically modified according to a wide range of information theory-enhanced cryptographic complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, a community member's XNA may be mutated (eg, permanently modified/updated). As explained, non-recurrent differentiation targets (e.g., XNA) may be derived from specific computationally complex genomic functions, and this differentiation consists of VDAX with various enclave relationships N×M. It may contain a digital ecosystem with dimensions N×M. Such digital ecosystem relationships may require changes in their differentiation attributes (eg, revocation of relationships), which is one of the most difficult problems in security management. Mutations in XNA allow specific (broad and narrow) redetermination of differentiating attributes, efficiently solving the problem of relational withdrawal. In embodiments, the XNA genome construction can be tailored to a particular digital ecosystem organization and the construction is modifiable. In some embodiments, XNA random vectors can be modified uniformly or subtly (broadly and narrowly) based on specific instructions. Modifications of XNA maintain the genomic integrity of the XNA constructs and their correlative attributes. In embodiments, VDAX carrying mutated XNA cannot affect future differentiation with VDAX carrying non-mutated XNA. In embodiments, XNA may be genetically mutated according to a wide range of information theory-facilitated cryptographic computational complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, a community member's CNA object 312 may be mutated (eg, permanently modified/updated). As explained, non-recurrent entitlement objects (e.g., CNAs or PNAs) may be derived from specific computationally complex genomic features, the modifications of which are derived from VDAX with various enclave relations N×M may contain a digital ecosystem with dimensions N×M. Such digital ecosystem relationships may require modification of differentiating attributes (e.g. relationship revocation), which is one of the most difficult problems in security management. Modifications of eligibility objects in the VDAX ecosystem maintain common computationally complex genomic functions. Such digital ecosystem relationships may require modification of its eligibility objects, preventing VDAX from establishing future or additional ecosystem relationships. CNA or PNA mutations allow redefinition of eligibility object identification (broad and narrow).

In embodiments, the CNA genome construction can be tailored to a particular digital ecosystem organization and the construction is modifiable. In some embodiments, CNA random vectors can be modified uniformly or subtly (broadly and narrowly) based on specific instructions. CNA modifications maintain the genomic integrity of the CNA construct and its eligibility-correlation attributes. In embodiments, VDAX possessing mutated CNAs cannot establish a future eligibility-correlation with VDAX of non-mutated CNAs. In embodiments, CNAs can be genetically mutated according to a wide range of information theory-driven computational complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, the PNA genome construction can be tailored to a particular digital ecosystem organization, and the construction can be changed. In some embodiments, the PNA random primitive polynomials can be modified uniformly or inconspicuously (broadly and narrowly) based on specific instructions. Modification of the PNA maintains the genomic integrity of the PNA assembly and its eligibility synchronization attributes. In embodiments, a VDAX possessing a mutated PNA is unable to establish future competent synchrony with a VDAX of an unmutated PNA. In embodiments, PNAs can be genetically mutated according to a wide range of information theory-enhanced computational complexity functions.

In embodiments, ecosystem ancestors (eg, ecosystem VDAX) can assign DNA to community members (eg, enclaves, cohorts, etc.). In embodiments, each particular DNA construct has a unique genomic relationship. LNA provides correlation, XNA differentiateability, CNA engagement-completeness, and PNA engagement-eligibility. The overall capacities driven by these structures derive substantially from their genome-mathematical structure relationships, which ultimately lead to specific VDAX assignments. These VDAX relationships may change according to specific alterations in DNA (eg, LNA, XNA, CNA, and PNA).

In embodiments, ecosystem ancestors (or suitable ancestor VDAXs) can assign LNAs to community members. In an embodiment, the LNA correlation capability belongs to all digital ecosystems with dimensions N×M consisting of VDAX, which may have various enclaves and cohort relationships N×M, such digital ecosystems include LNA It also has the ability to correlate. In embodiments, LNA genome-based constructs are assigned to specific digital ecosystem VDAXs (eg, ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) to determine their associated correlation capabilities. In embodiments, the LNA assignments preserve the genomic integrity of the LNA constructs and their correlated attributes. In embodiments, VDAX with modified initial LNA assignments (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) can no longer affect correlation with VDAX carrying unmodified LNAs, but the same It may now be possible to influence future correlations with other VDAXs with modified LNA assignments. In embodiments, LNAs may be genomically assigned according to a wide range of information-theoretically-driven computational complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

In embodiments, an ecosystem ancestor (or suitable ancestor VDAX) can assign XNAs to community members. In embodiments, the XNA differentiation potential belongs to all digital ecosystems with dimension N×M consisting of VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX), which also have various enclaves and cohort relationships N×Ma. In embodiments, constructs based on the XNA genome are assigned to specific digital ecosystem VDAX (eg, ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) to determine their relative differentiation potential. In embodiments, assignment of XNAs preserves the genomic integrity of the XNA constructs, as well as their differentiation attributes. In some embodiments, VDAX with modified initial XNA allocation (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) are no longer able to affect differentiation with VDAX carrying unmodified XNA. may have become able to influence differentiation with other VDAXs with the same modified XNA allocation. In embodiments, XNAs may be genomically assigned according to a wide range of information-theoretically-driven computationally complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, ecosystem ancestors (or suitable ancestor VDAXs) can assign CNAs to community members. In embodiments, the CNA Engagement-Integrity Capability belongs to all digital ecosystems with dimension NxM consisting of VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX), and this digital ecosystem The system may also have various enclaves and cohort relationships NxMa. In embodiments, CNA genome-based constructs are assigned to a particular digital ecosystem VDAX (eg, ecosystem VDAX, enclave VDAX, cohort VDAX, and/or dependent VDAX). In embodiments, these CNA genome-based constructs determine their relative engagement-integrity capabilities within the ecosystem. In some embodiments, CNA genome-based constructs assigned to a particular digital ecosystem VDAX may also be unique.

In embodiments, the CNA assignment preserves the genomic integrity of the CNA assembly and its engagement integrity attributes. In some embodiments, VDAXs with modified initial CNA assignments (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) can no longer impact engagement integrity with VDAXs harboring non-modified CNAs. not, and now may be able to affect engagement integrity with other VDAXs with the same revised CNA assignment. In embodiments, CNAs may be genomically assigned according to a wide range of information theory-driven computational complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In embodiments, ecosystem ancestors (or suitable ancestor VDAXs) can assign PNAs to community members. In embodiments, the PNA Engagement Eligible Capabilities belong to all digital ecosystems with dimensions N×M composed of VDAX (e.g., Ecosystem VDAX, Enclave VDAX, Cohort VDAX, and/or Dependent VDAX), which are It is also possible to have different enclaves and cohort relationships NxMa. In embodiments, PNA genome-based constructs are assigned to specific digital ecosystem VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and/or subordinate VDAX) to determine their associated engagement-eligibility capabilities. do. In embodiments, PNA genome-based constructs assigned to a particular digital ecosystem VDAX may also be unique.

In embodiments, PNA assignments preserve the genomic integrity of the PNA constructs and their engagement-eligibility attributes. VDAXs with modified initial PNA assignments (e.g. Ecosystem VDAX, Enclave VDAX, Cohort VDAX, etc.) can no longer affect engagement eligibility with VDAXs carrying unmodified PNAs and are now May be able to affect eligibility to engage with other VDAXs with assignments. In embodiments, PNAs may be genomically assigned to VDAX according to a wide range of information theory-driven computationally complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

As previously mentioned, sets of sufficiently correlated VDAXs can be engaged using links. In embodiments, the primary purpose of the link is to allow VDAX pairs to exchange the information necessary to perform higher-level computationally complex genomic functions. In embodiments, the information exchanged on the link is called genome-engagement-cargo (GEC). In embodiments, link processing may include link generation, link hosting, and link updating. Link generation may refer to the generation and transport of links by spawning VDAX. Link hosting may refer to the acquisition and integration of information contained in a link by the receiving VDAX. Link updating may refer to a CG process that alters the genomic underpinnings used by VDAXs to engage with other VDAXes. The process of link updating is sometimes referred to as "link correction". In an embodiment, the linking process (spawning, hosting, updating) depends on the specific information theory configuration. For example, in embodiments, LNAm may be used as the basis for genome correlation, CNAm may be used as the basis for genome engagement-completeness, and PNA may be used as the basis for genome engagement-eligibility. may be used). These DNA constructs (eg, LNA, CNA, and PNA) are application-specific genomic constructs that enable specific genome conversion functions that facilitate the linking process. In embodiments, the link process may be defined in the link module 430 of the CG-ESP, whereby some or all of the CG-ESP instances are configured with link process module 430 instances that perform these functions. may For example, any VDAX that needs the responsibility of creating, hosting, and/or updating links may define processes for static and/or dynamic linking, such link process module 430 It may consist of instances.

In embodiments, a pair of VDAXs (eg, a first VDAX and a second VDAX) belonging to the same CG-enabled digital ecosystem can spawn and host links without prior agreement. In these embodiments, a VDAX intended to produce a genome (e.g., a first VDAX) is a VDAX intended to produce a genome (e.g., a second VDAX) is intended to produce a genome. Intend. A link for receipt and use of a genome engagement cargo (GEC) by another VDAX (e.g., a second VDAX) must be engagement-consistent with another VDAX (e.g., a second VDAX) for which the link was generated. It utilizes its CNA to establish In some embodiments, a VDAX (e.g., a first VDAX) generates a genome link for receipt and use of the contained GEC by another VDAX (e.g., a second VDAX), thereby A set (eg, first and second VDAX) may have multiple genomic links that utilize the same CNA to establish engagement-integrity.

In embodiments, a VDAX (e.g., a first VDAX) seeking to generate a genomic link for receipt and use of a GEC by another VDAX (e.g., a second VDAX) will The PNA can be used to establish engagement eligibility with In some embodiments, a VDAX (e.g., a first VDAX) generates a genome link for receipt and use of the containing GEC by another VDAX (e.g., a second VDAX), thereby A pair (eg, first and second VDAX) may have multiple genomic links that utilize the same PNAto establishing engagement-eligibility. Note that in some embodiments, the GEC included in the link may contain additional link activation requirements.

In some embodiments, a generating VDAX that is generating a link for transmission and use (e.g., "link hosting") by another VDAX (e.g., a second VDAX) is Its LNAto can be utilized to establish genomic correlations with VDAX. As explained, LNA-based genomic processes may enable entire digital ecosystems (communities) to achieve correlations between VDAXs based on a single genomic structure (e.g., LNA). In embodiments, LNA-based genomic processes allow VDAX to modify its respective LNA architecture by using specific computationally complex functions, whereby these LNA-based genomic processes are Make use of informational substructures (eg, LNA-based genomic substructures). In embodiments, LNA-based genomic substructures compute unique transformation information by VDAX-producing links that can only be reproduced by VDAX-hosting links at the same level of entropy as the underlying computationally complex genomic functions. can be used to In embodiments, unique genomic engagement factors are utilized to prepare GECs for digital transport from spawning VDAX to hosting VDAX. In some of these embodiments, the link-hosting VDAX may use unique genomic engagement factors to decode the encoded GRI contained in the GEC. In some embodiments, a unique genomic engagement factor may be rendered as multiple sub-configurations for application in multiple digital transmission channels.

In some scenarios ecosystem correlation is not available. In some embodiments, VDAX authentication may be required for link spawning and hosting when ecosystem correlation is not available. In these embodiments, VDAX validation may be achieved through the use of alternate genomic substructures to facilitate free-form correlation (FFC). For example, a scenario can arise where a pair of VDAXs are in a unique genome-digital ecosystem (which is sometimes called a "republic"). In some embodiments, these unrelated VDAXs can form a unique genome digital ecosystem (sometimes called a "federation") for specific operations and applications. In these embodiments, VDAX may originate links as members of federations as well as within their respective republics.

In embodiments, the link-spawn genomic process may be performed according to a wide range of information theory-enhanced computational complexity functions that drive the execution of genomic functions and processes. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

As mentioned above, genome link hosting (or "link hosting") is a VDAX (e.g., , second VDAX) acquisition and integration of link information. In embodiments, link hosting may be performed according to specific computationally complex genomic functions. In embodiments, a hosting VDAX (eg, a second VDAX) receives the unique transform information substructure via one or more digital transmission channels. In embodiments, a hosting VDAX (e.g., a second VDAX) that seeks to use (host) a genome engagement cargo (GEC) transported by a link created by a spawn VDAX (e.g., a first VDAX) is Its CNA can be used to establish engagement integration with VDAX. In an embodiment, when a hosting VDAX (e.g., a second VDAX) intends to use (host) a genome-engage cargo (GEC) transported by a link generated by a spawning VDAX (e.g., a first VDAX) , can utilize its PNA to establish engagement-eligibility with the spawning VDAX.

In embodiments, a hosting VDAX may leverage an LNA that enables correlation of its digital ecosystem by modifying its LNA with a specific computationally complex function that utilizes a unique transformation information substructure. can. In embodiments, LNA-based genome substructures are utilized to compute intrinsic genome engagement factors with link-hosting VDAX at the same level of entropy as the underlying computationally complex genome function. In embodiments, the hosting VDAX (eg, the second VDAX) utilizes unique genomic engagement factors to extract GECs from links provided by the spawning VDAX (eg, the first VDAX). In embodiments, a hosting VDAX (e.g., a second VDAX) may be required to complete additional link activation requirements imposed by the spawning VDAX, whereby the additional link activation requirements are GEC provided in

As mentioned earlier, scenarios can arise where a pair of VDAXs are in a unique genome-digital ecosystem (which is sometimes called a "republic"). In some embodiments, these unrelated VDAXs may form a unique genome digital ecosystem (which may be referred to as a "federation") for a particular operation, as described above. may be used for

In embodiments, the link-hosting genome process may be performed according to a wide range of information theory-driven computational complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions. In embodiments, these functions may be required to perform genomic operations.

In embodiments, a VDAX may update links hosted by other VDAXes. For example, to increase the level of security, a VDAX may be hosted by other VDAXes to reduce the likelihood of malicious parties determining or otherwise obtaining linking information (e.g., GRI). You may update the links that In these embodiments, a pair of VDAXs (e.g., a first VDAX and a second VDAX) that previously completed the "link generation" and "link hosting" protocols can update one or both links. . In this way, a VDAX (e.g., a first VDAX) may change the genomic basis used to engage another VDAX (e.g., a second VDAX) and/or vice versa. It is possible. In some embodiments, new genome links generated by a VDAX (e.g., a first VDAX) and submitted for hosting on another VDAX (e.g., a second VDAX) are hosted by the other VDAX. may be used to replace one or more existing links with newly created links, thereby updating the links. In embodiments, a link spawned by a VDAX that is submitted for hosting on another VDAX may be used to amend some or all of the GRI data of an existing hosted link. may be

As noted above, scenarios may arise where a pair of VDAXs are in a unique genomic digital ecosystem (which is sometimes called a "republic"). In some embodiments, these unrelated VDAXs may form a unique genome digital ecosystem (or "federation") for specific operations and applications. In some of these embodiments, the VDAX federation may also update their links for specific operations and uses.

In embodiments, the link update genome process may be performed according to a wide range of information theory-facilitated cryptographic complex functions. In embodiments, these information-theoretically facilitated functions may be cryptographic-based, non-cryptographic, or hybrid computational complexity functions required to perform genomic functions and processes.

As described throughout this disclosure, sequence mapping and binary conversion are CG operations that may be performed to form a VBLS. In embodiments, sequence mapping may be performed on public and/or private sequences. In embodiments, a sequence may refer to a sequence of data (eg, a sequence of bits). In embodiments, public sequences may refer to public protocol and format dependent information (e.g., TCP, UDP, TLS, HTTP, H.265, etc.), while private sequences may refer to private and/or proprietary protocols and formats. It may refer to format dependent information, and such information may refer to public and private sequences. In embodiments, sequences (eg, public or private sequences) are computationally transformed to non-recursive values. Although sequences may be widely heterogeneous (e.g., protocol-independent and have pre-existing entropy), sequences are processed in such a way as to yield values with a particular level of entropy. In embodiments, this process is compatible with a wide range of protocols and formats, and may be started with different sequences that represent each existing entropy. In embodiments, this process may be performed using complex genomic processes and functions that result in genomic engagement factors exhibiting a particular level of entropy.

In embodiments, CG-based security management systems and architectures may require the use of genomic engagement factors in conjunction with genomic data construction. In embodiments, these genomic engagement factors may be derived in part through the use of repetitive data (eg sequences). Prior to using the sequences in combination with genomic data construction, the sequences are processed so that the entropy of the resulting genome engagement factor matches the entropy of the genome construction (eg, XNA). This process is called "sequence mapping" and the products are called genome engagement factors. Even if the sequences are widely disjointed, the resulting genomic engagement factors exhibit a certain level of entropy. In embodiments, genomic engagement factors may be generated from integration of XNA vectors. In some embodiments, multiple genome engagement factors may be produced from a set of XNA vectors. In some embodiments, this process may be important for open-architecture applications that rely on the transformation of specific digital objects, which are potentially disparate protocols and formats (e.g., TCP, UDP, TLS, HTTP , H.265).

In some embodiments, widely different external formats and protocol resident data are used to build sequences without modification. In some embodiments, widely disparate external formats and protocol-resident data, with modifications, are used to construct sequences. In some embodiments, sequencing is used in combination with a specific genome-based data construction to determine unique vectors exhibiting a specific entropy. In some embodiments, sequences are mapped according to computationally complex genomic processes and functions in combination with specific genomic data constructs to derive specific genomic engagement factors. In embodiments, sequence mapping derives genome engagement factors that exhibit an entropy that matches the entropy of the genome data construction, regardless of the inherent entropy of the sequence. In some embodiments, genomic engagement factors may be generated from sequence mapping utilizing internal CG-ESP formats and protocols in combination with these external formats and protocols. Note that genome engagement factors should be determined in a manner that cannot be exploited (e.g., using computationally complex functions) to reveal resident data and genome-based construction of formats and protocols.

In embodiments, sequence mapping performs a genome engagement factor-generated genomic process computed according to information theory-facilitated computational complexity functions. In embodiments, these information-theoretically-enhanced functions can be used for cryptographic-based, cryptographic-free, or hybrid computation by generating unique genomic engagement factors for sequences (published or private) and XNA. It can be a complex function.

In some embodiments, CG-ESP may implement genomic information theory driven processes to facilitate hyperscalable correlation. In embodiments, virtual authentication (eg, unique correlation) of ecosystem, enclave, and cohort involvement relationships may be achieved with hyper-scalable correlation. As described, hyperscalability techniques can be used to strongly enhance the engagement of ecosystems, enclaves, and cohorts that rely on accurate and unique correlations. As explained, organic ecosystems (e.g., biological ecosystems) exhibit strong, albeit borderline, interrelationships among species, progeny, and siblings that derive from complex biochemical processes. Prove it. The principles that govern these biochemical processes are reflected by specific digital genome structures facilitated by information theory and can exhibit unique correlations across ecosystems, enclaves and cohorts. In embodiments, the digital genome correlation is substantially unlimited and exhibits a certain, user-controllable entropy. In embodiments, genome eligibility objects (eg, CNA and/or PNA) and genome correlation objects (eg, LNA) may be used for digital genome correlation.

In embodiments, ecosystem VDAX can utilize computationally complex genomic processes to achieve virtual affiliation with enclaves and cohorts. Similarly, enclave VDAX may take advantage of these computationally complex genomic processes to achieve hyper-scalable correlation with cohorts, and cohort VDAX may take advantage of computationally complex genomic processes to achieve hyperscalable correlations with other Hyper-scalable correlation with cohorts may be achieved. In embodiments, inherent hyperscalable correlations between ecosystems, enclaves, and cohorts may be modified by computationally complex genomic processes. For example, an ecosystem VDAX may modify the LNA for a given enclave to prevent future link exchanges in that particular enclave for one or more members of the enclave. In some embodiments, a given ecosystem member enclave VDAX and cohort VDAX is computationally complex to correlate engagement with other ecosystem members enclave VDAX and cohort VDAX. Genome processing may be employed. For example, in some embodiments, two ecosystem VDAXs may form genomic datasets derived from their respective genomic datasets, whereby members of the ecosystem may share the derived genomic data (or derivatives thereof). organisms) can be used to engage across ecosystems. In this way, enclave VDAX and cohort VDAX are able to achieve unique hyper-scalable correlations across multiple ecosystems based on computationally complex genomic processes and respective genomic datasets. In embodiments, the hyperscalable correlation performs computational genomic processes according to a wide range of information-theoretically-driven computational complexity functions, with PNAs, CNAs, and LNAs generating unique genomic engagement factors. These functions can be cryptographic-based, non-cryptographic, or hybrid computational complexity functions.

In some embodiments, CG-ESPs may implement processes facilitated by genomic information theory to facilitate hyperscalable differentiation. In some examples, hyper-scalable differentiation may or may be required to provide unique affiliations between ecosystems, enclaves, and cohorts based on digital network fostering relationships. In embodiments, hyperscalability techniques can be used to strongly enhance ecosystem, enclave, and cohort affiliation that rely on precise and unique differentiation. Some exemplary organic ecosystems may show limited but strong evidence of differentiation across species, progeny, and siblings that can result from complex biochemical processes. The principles governing the biochemical processes in these examples are reflected by specific digital genome structures governed by information theory, and can exhibit inherent differentiation across ecosystems, enclaves, and cohorts. In some instances, this digital genome differentiation may be substantially unrestricted and exhibit a certain, user-controllable entropy.

There may be various exemplary implementations for applying hyperscalable differentiation in ecosystems, enclaves, and/or cohorts. For example, members of a CG-enabled ecosystem may leverage computationally complex genomic processes to achieve hyper-scalable differentiation that facilitates unique non-recursive virtual affiliations between ecosystems, enclaves, and cohorts. . In some instances, CG-enabled enclaves leverage computationally complex genomic processes to achieve hyperscalable differentiation that facilitates unique non-repetitive virtual affiliations between enclaves and cohorts. In some instances, cohorts can take advantage of computationally complex genomic processes to achieve hyper-scalable differentiation that facilitates unique non-repetitive virtual affiliations between cohorts. In embodiments, unique hyper-scalable differentiation between ecosystems, enclaves, and cohorts may be altered by computationally complex genomic processes. In some embodiments, enclaves and cohorts that are members of a given ecosystem may employ computationally complex genomic processes to partner with enclaves and cohorts that are members of other ecosystems. In this way, enclaves and cohorts, according to some embodiments of the present disclosure, are capable of achieving unique virtual affiliations across multiple ecosystems based on computationally complex genomic processes. obtain. In some examples, the hyperscalable differentiation is computed according to a wide range of information theory-facilitated cryptographic-based, non-cryptographic, or hybrid (e.g., cryptographic-based and/or non-cryptographic) computational complex functions. A genomic process may be performed whereby sequencing and XNA generate unique genomic engagement factors that can be used to generate VBLS.

In some embodiments, the CG-ESP may implement processes facilitated by genomic information theory to facilitate virtual agility. In some examples, virtual agility entails the ability to perform hyperscalable differencing and hyperscalable correlation at the network (e.g., Open Systems Interconnection (OSI)), software stack level, and/or hardware components. may provide unique engagement between ecosystems, enclaves, and cohorts that may be Both network and software engagement traditionally require the creation, negotiation, and maintenance of session-based protocols. In some instances, these protocols are computationally expensive and may limit adoption options for networks and software stacks. Virtual agility can enhance engagement of ecosystems, enclaves and cohorts by strongly removing at least some of the requirements of session-based protocols. Virtual agility may reflect specific digital genome structures that are generated by processes facilitated by information theory and exhibit virtually unlimited, specific, and user-controllable entropy.

There may be various example implementations for applying virtual agility in ecosystems, enclaves, and/or cohorts. For example, virtual agility can be employed at the network stack level, software stack level, and/or hardware level, thereby supporting multiple ecosystems, enclaves, and/or cohorts. In embodiments, virtual agility may eliminate the requirement to create, negotiate, and maintain session-based protocols for network communication engagement, software application engagement, and/or hardware component engagement. .

In some embodiments, the CG-ESP may implement a genomic information theory-enhanced process to generate and/or decode VBLS. As explained, CG-ESP may allow digital objects with specific formats and protocols (e.g., packets, sectors, sequences, and frames) to be computationally transformed into VBLS objects via link exchanges (e.g., link spawn and/or link host) and sequence mapping. In embodiments, the VBLS objects produced by this process may be unique, non-reproducible, and/or computational quantum proofs. In some embodiments, VBLS may be the finished product of linking, sequencing, correlation, differentiation, and agility functions and processes controlled and facilitated by genomic information theory. Computationally and quantum-proven VBLS can form the basis for building specific network, software and hardware architectures, either in current or newly developed deployments. .

There may be various exemplary implementations for applying Virtual Binary Language Scripts (VBLS) in ecosystems, enclaves, and/or cohorts. For example, VBLS can allow extensive and highly flexible control of complementation of ecosystem, enclave, and/or cohort relationships. In embodiments, VBLS can facilitate the completion and control of dynamic genome-based architectures. In some examples, a VBLS rendered digital object can be a unique, non-recurring, and computationally quantum proof while eliminating the need for private key generation, exchange, and maintenance. VBLS rendered objects may require minimal overhead and bandwidth for VDAX(s) engagement. In some examples, VBLS rendering objects can represent ecosystem, enclave, and/or cohort-oriented genome modifications. In embodiments, VBLS applications may be protocol agnostic (eg, interoperable with network, software, and/or hardware solutions). In an example, VBLS is a unique, non-recurrent, computationally quantum proof engagement between community members (e.g., intra-ecosystem, inter-ecosystem, ecosystem to cohort, cohort to cohort, and/or cohort to cohort engagement). In some exemplary embodiments, any VBLS-enabled VDAX can participate in multiple VBLS relationships with other VDAX(s). In these embodiments, the VDAX may form a unique relationship with each VDAX. In some embodiments, the genome engagement factor used for generation may be used simultaneously for primary and secondary applications that also require unique non-recursive values at a particular entropy.

In embodiments, VDAX may be configured to engage symmetric and/or bisymmetric VBLS-based engagements. For example, in some embodiments, VBLS-enabled VDAX(s) engage based on link exchanges (e.g., spawn and host) that can use genome linking instructions and genome structure, which may be the same. , can yield symmetric-based engagement. In embodiments, VBLS-enabled VDAX can perform bisymmetric engagements based on highly correlated genome constructs (eg, identical or other well-correlated XNAs). In these embodiments, VBLS-enabled VDAXs exchange links containing unique genomic regulation instructions (GRIs). However, in some scenarios, VBLS-enabled VDAX can engage symmetrically if the link exchanges contain the same GRI. For example, CG-ESP can be configured to perform a one-way link exchange whereby one VDAX can provide GRI for use by both VDAXes in the VBLS generation process. In this manner, a VBLS-enabled VDAX can engage with other VDAXes based on symmetric and/or bi-symmetric binary languages without iterative coordination between VDAXes. In some of these embodiments, VBLS-enabled VDAX engagement can proceed without negotiation of a formal session because its symmetric or bisymmetric binary language simultaneously encapsulates authentication, integrity, and privacy. .

The rapid expansion of remote network-centric highly distributed solutions and services (e.g., remote and edge clouds) is an open or semi-open environment, leaving sensitive binaries exposed to untrusted third parties (e.g., adversaries). creating a situation in which it is possible that Homomorphic encryption (handles data in an encrypted state), functional obfuscation (handles data and application code in an encrypted state), various trusted execution environments (physical and software isolation of executable code, etc.) ) is the current approach to resolving these critical exposures. Although these methods may be significantly improved, none of them have a significant performance impact and do not address the significant scalability required for widespread commercial applications.

According to some embodiments of the present disclosure, the CG-ESP genome information theory technology is a computational quantum proof, highly efficient, and hyperscalable virtualization for processing data and application code that can be organized as a genome ecosystem. Enable trusted executable regions. In some of these embodiments, the virtual trusted execution domains are applications (e.g., APIs, libraries, and threads), operating systems (e.g., kernels, services, drivers, and libraries), and system-on-chips (e.g., Enables unique transformation of component resident executable binaries and data such as processing units (eg, cores). In embodiments, the CG-ESP Executable Isolation Component (EIC) facilitates Component-Binary-Isolation (CBI) necessary for the necessary transformations. In some embodiments, segregation is enabled by 1) inherent genomic correlations between dispersed components belonging to the same ecosystem, and 2) inherent genomic differentiation with components of other viable ecosystems. . This process of correlation and differentiation forms the basis on which Virtual Trusted Execution Domains (VTED) enable highly flexible and scalable Component Binary Isolation (CBI).

In embodiments, hyperscalable differentiation enables highly flexible Component Binary Separation (CBI) of ecosystems, enclaves, and cohorts. In some of these embodiments, isolation of virtual trusted execution domains (VTEDs) may be achieved through the sharing of genomic components within VTEDs and their CBI with the VTED ecosystem VDAX, thereby allowing these Hierarchical links are established between members. In embodiments, VTED provides a functional alternative to isomorphic encryption where the CBI is held static and run-time operations are performed on the encoded binary and associated data.

In embodiments, VTED virtual agility enables highly flexible component binary isolation (CBI) and control of dynamic genome-based architectures. In further embodiments, genome correlation and differentiation enable dynamic genome-based systems to construct dynamic genome network topologies without the need to modify the physical operating environment.

In embodiments, the VTED transformed executable binary is unique, non-reproducible, and computationally quantum proof. In embodiments, this transformation eliminates the requirement for private key generation, exchange, and maintenance often required by Trusted Execution Environment (TEE) technologies. In embodiments, the VTED executable binary is transformed through the application of genomic constructs (eg, LNA or ZNA) to build the transformed executable binary.

In embodiments, VTED's hyperscalable correlation, hyperscalable differentiation, and hyperscalable agility uniquely enable CBI to operate with minimal overhead and bandwidth. In a further embodiment, multiple genome construction is applied to a large number of CBIs providing hyperscalability of the VTED ecosystem.

In embodiments, VTED-enabled CBIs for ecosystems, enclaves, and cohorts can be directly genomically modified without compromising binary executable relationships. In embodiments, VTED-enabled CBI modifies binary information while maintaining VTED's ability to run CBI within its ecosystem.

In embodiments, the VTED-enabled CBI may be compatible with known crypto-based and crypto-free computational methods. In a further embodiment, compatibility of VTED and CBI with crypto-based and non-crypto computational methods is maintained through transparent genome construction-based transformations.

In embodiments, VTED may enable unique, non-recurring, computationally quantum-proof CBI feasibility among specific ecosystems based on its unique computationally complex genome construction and processes. In a further embodiment, the CBI assembly process applies genome assembly that does not rely on conventional computationally expensive manipulations.

In embodiments, VTED may allow CBI executables to have a number of properties, including unique, non-recurring, computational quantum proof engagements between specific ecosystems and enclaves. In embodiments, CBI executables exhibit these properties based on their unique computationally complex genome construction and processes. In a further embodiment, VTED applies genome structure to develop CBI executables that enable quantum proof operations between CBI executables and genomic VTED.

In embodiments, VTED may enable CBI feasibility based on its unique computationally complex genomic architecture and processes. In further embodiments, in scenarios where the VTED includes multiple cohorts across multiple enclaves, the VTED may have certain desirable properties such as being unique among entities, non-reproducible, and quantum proof. Genomic components can be applied to enable certain CBI executables. In a further embodiment, an ecosystem VDAX is a genome architecture-based CBI license in which individual cohorts can have specific capabilities or CBIs enabled for operation within that ecosystem, enclave, or cohort. model can be provided.

Referring now to FIG. 6, an exemplary CG-enabled digital ecosystem 600 is depicted in accordance with some exemplary embodiments of the present disclosure. An example configuration of the CG-enabled digital ecosystem 600 depicted in FIG. 6, including the depicted security platform topography and architecture, is provided as a non-limiting example and is intended to limit the scope of the present disclosure. Note that this is not intended. As discussed throughout this disclosure, the composition of CG-ESPs may be defined by community owners of the digital ecosystem. When referring to the “community owner” of a digital ecosystem, the term refers to the entity that manages, maintains or owns the community, its representatives (e.g. network administrators, CIOs, IT administrators, landlords, consultants, security experts, artificial intelligence software on behalf of community owners, or any other suitable representative), and/or other suitable may refer to a party

In embodiments, a set of VDAXs (eg, VDAXs 608, 610, 612, 614) perform a set of genomic security functions on behalf of the digital ecosystem 600. Note that VDAX may also be called "CG-Security Controller" or "Security Controller". In an embodiment, the CG-enabled digital ecosystem 600 includes a set of enclaves 602 and for each enclave a set of respective cohorts. It is noted that a general reference to CG-ESP may be a reference to the composition of the VDAX participating in the digital ecosystem (e.g. Ecosystem VDAX608, Enclave VDAX610, Cohort VDAX612, and/or Dependent VDAX614). be. In embodiments, the set of cohorts can include independent cohorts 604 . As described, an independent cohort 604 may include a collection of one or more devices operating as independent entities. Examples of independent cohorts 604 include, but are not limited to, grids, networks, cloud services, systems, computers, appliances, devices, IoT devices, and the like. In some embodiments, the set of cohorts may further include dependent cohorts 606 . A dependent cohort 606 may refer to an individual digital entity that is realized by a digital container-based VDAX or for which an independent cohort acts as a proxy. Examples of dependent cohorts include, but are not limited to, sensors, applications, data, files, databases, media content, cryptocurrencies, smart contracts, and the like. An enclave 604 may be a collection of two or more cohorts (eg, independent cohort 604 and/or dependent cohort 606) that have mutual identities of interest. As explained, mutual identity of interest may be any logical commonality between cohorts within an enclave. For example, a cross-interest identity may be a collection of devices, servers, documents, applications, etc. used by business units within an enterprise organization. In another example, mutual identities of interest may be devices, documents, applications, etc. belonging to a single family or user. In another example, the mutual identities of interest may be a set of autonomous vehicles traveling on a particular grid. In embodiments, the geography of the digital community (and the corresponding architecture of the CG-ESP) may be defined by the community owner in consideration of these mutual interest identities. In embodiments, eligibility for membership in the ecosystem 600 and/or its one or more enclaves 602 may be defined by a community owner, membership and revocation thereof may be defined by the community owner and/or It may be governed according to one or more sets of rules. In some embodiments, the respective architecture of a particular CG-enabled digital ecosystem and corresponding CG-ESP is designed so that community owners purchase or otherwise obtain a pre-configured digital ecosystem with default configurations. , may be defined according to the default configuration.

In some embodiments, ecosystem descendants (e.g., ecosystem VDAX 608) are configured to build one or more enclaves 602, each enclave according to an architecture and configuration defined by the community owner. Each set of cohorts may be added to 602 . In some embodiments, the architecture and configuration for CG-enabled digital ecosystem 600 may be defined by CG-ESP (eg, as described in FIG. 4). In these embodiments, the VDAXs participating in the digital ecosystem 100 allow each VDAX to perform its respective role with respect to the ecosystem 600 and the intended ecosystem. Each instance of CG-ESP can be run that forms a relationship with a member. For example, the set of VDAXs can be an ecosystem VDAX608 that plays an ecosystem level, one or more enclaves VDAX610 that play an enclave level, one or more cohorts VDAX612 that play a cohort level, and/or subordinate cohort roles. any suitable combination of one or more subordinate VDAX 614 that fulfills the For example, in some exemplary implementations, the ecosystem VDAX608 generates, assigns, and persistently modifies genomic data of other ecosystem members, confirms engagement eligibility, exchanges links, and distributes VBLS. cohort VDAX612 (e.g., via a cohort CG-ESP instance) can be configured to generate (e.g., via a CG-ESP instance) the genomic data about the ecosystem to other cohorts or It may not have the ability to assign, but is configured to verify engagement eligibility, exchange links, and generate VBLS.

In embodiments, VDAX may be implemented as any combination of software, hardware, firmware, and/or middleware that performs a specific set of genomic functions for an ecosystem. Note that the existence of dependent cohorts 606 depends on at least one independent cohort (e.g., a file depends on the device on which it is stored, or an application instance depends on the device on which the application is run). ). Accordingly, in some embodiments, the dependent VDAX 614 of a dependent cohort 606 (e.g., files, media content, applications, etc.) is a computing device, server, cloud system, etc.).

In embodiments, the ecosystem VDAX 608 performs security-related functions of the digital ecosystem, requiring subsequent interaction with the enclave or its cohorts after the ecosystem VDAX 608 initializes and assigns the enclave its genomic datasets. Because they do not, they may be considered “ancestors” of the ecosystem. In embodiments, an ecosystem may be configured to allow for independent sub-ecosystems, with functional ecosystem-level VDAX capabilities, but multiple low-level VDAXes derived from the primary ecosystem VDAX608. Note that it may contain

In embodiments, ecosystem VDAX 608 digitally generates each genomic dataset for one-time distribution to ecosystem enclaves 602 and/or cohorts 604 , 606 within respective enclaves 602 . Genomic data objects within a genomic data set can have similar or identical structures, mathematical capabilities, and/or entropy levels, but each serve different purposes. In embodiments, genomic eligibility objects (eg, CNA or PNA objects) provide core genomic capabilities for community members (eg, enclaves or cohorts) to computationally correlate individual ecosystem identities. In embodiments, genomic correlation objects (eg, LNA objects) provide the ability for link exchange between members (eg, ecosystem-to-enclave, enclave-to-enclave, enclave-to-cohort, cohort-to-cohort, etc.), which is what controls a member's ability to establish engagement with another member. In embodiments, a genome differentiation object (eg, an XNA or ZNA object) provides the capability for bisymmetric communication between members based on VBLS. In embodiments, the CNA, PNA, LNA, and XNA digital genome structures are complex and unique. In embodiments, CNA, LNA, XNA, and PNA may be derived using complex mathematical functions.

According to some embodiments of the present disclosure, ecosystem VDAX 608 may generate genomic datasets that it assigns to itself. A genomic data set may contain one or more different types of genomic data objects. For example, in some embodiments, the ecosystem VDAX includes genomic eligibility objects (e.g., CNA objects and/or PNA objects), genomic correlation objects (e.g., LNA objects), and genomic differentiation objects (e.g., XNA objects). or ZNA objects) can be generated according to the platform instance requirements (e.g., the types of genome objects, the entropy level of each genome object, and the specific algorithms used to generate such genome data objects). be. In embodiments, the genomic dataset initially generated by ecosystem VDAX 608 and allocated across ecosystem 600 may be the genomic dataset from which all progeny genomic datasets of digital ecosystem 600 originate. For convenience of explanation, ecosystem offspring genomic datasets are sometimes referred to as "offspring genomic datasets" (or "offspring DNA sets"). In some embodiments, ecosystem VDAX608 may first generate an ancestral genome dataset. For example, ecosystem VDAX 608 may generate for each ancestral genome data object a respective binary vector with a particular dimension.

In some embodiments, the ecosystem VDAX608 may generate respective progeny genome datasets for each enclave from the progenitor genome datasets. In some embodiments, the ecosystem VDAX608 modifies the progenitor genome datasets using a predefined set of genome manipulations, and then progeny genome datasets (or “enclave data set"). In some embodiments, the ecosystem VDAX608 may generate respective progeny genome datasets for each enclave from the progenitor genome datasets. In some embodiments, the ecosystem VDAX 608 may modify the progeny genome dataset using a predefined set. For example, the ecosystem VDAX608 modifies the offspring genome eligibility objects of the offspring genome dataset using computationally complex functions to obtain different enclave genome eligibility objects for each enclave in the ecosystem. You may Modifying an ancestral correlation object of an ancestral genome dataset using a computationally complex function to obtain a different enclave correlation object for each enclave in an ecosystem, and ancestry discrimination of an ancestral genome dataset using a computationally complex function. modifying the differentiation object to get a different enclave differentiation object for each enclave in the ecosystem. In embodiments, since different types of genomic objects may be implemented in different types of data structures and/or may be required to exhibit different properties, the techniques by which different types of genomic objects are modified include: can be different. Different correction techniques are described throughout this disclosure. Note that in some implementations of security platforms there may only be a single enclave. Depending on the various techniques implemented in a particular CG-ESP, certain types of genomic objects (e.g. LNA and XNA) may be highly correlated (e.g. identical or otherwise sufficient) with some or all enclaves. correlations), and other types of genomic objects (eg, CNAs or PNAs) may be unique to each respective community member but still sufficiently correlated. Correction of one or more other portions of the genome dataset and subsequent progeny genome datasets, even if some types of genome objects in the progeny genome dataset are not modified from corresponding genome objects in the progeny genome dataset Assignment to offspring community members (e.g., enclave or cohort) also means that offspring genome datasets (e.g., enclave genome datasets or cohort genome datasets) can be assigned to any one or more genome objects in the offspring genome dataset. Sometimes referred to as "derived" so that it can be said to have been derived from a progeny genome dataset (e.g., a progeny genome dataset or an enclave genome dataset) even if it has not been modified from the progeny genome dataset.

In embodiments, the enclave VDAX610 is configured to add cohorts to the corresponding enclave by modifying the enclave genome dataset of the corresponding enclave and assigning the resulting progeny genome datasets to the respective cohorts within the enclave. may In some embodiments, enclave VDAX 610 may generate a cohort genomic dataset for each new independent cohort 604 added to enclave 602 . In some embodiments, an enclave VDAX 610 generates a unique, but highly correlated, genome-qualifying object (e.g., CNA) for each independent cohort 604 added or to be added to the corresponding enclave 602. CG-ESP can be configured to generate. In some of these embodiments, ecosystem VDAX 608 or enclave VDAX 610 may generate genome eligibility objects such that any set of cohorts within the enclave has a unique correlation of genome eligibility objects. . For example, in some embodiments, each cohort of enclave 602 is based on the genomic qualifications of the offspring (e.g., ecosystem or enclave) objects such that cohorts are unique while maintaining a high level of correlation. The generated genome eligibility object is assigned. In this way, any pair of cohorts can confirm their eligibility to engage with each other based on the correlation of their respective genomic eligibility objects. In some embodiments, members of an enclave (eg, cohort VDAX) are assigned highly correlated (eg, identical or otherwise well-correlated) genomic correlation objects and genomic differentiation objects. In some embodiments, pairs of cohorts may authenticate each other based on each cohort's respective genomic correlation object and may distinguish from other cohorts based on each cohort's respective genomic differentiation object. . In embodiments, the cohort genomic correlation object and the genomic differentiation object may be separate objects (although they may be similar or identical in structure). Alternatively, in some embodiments, the cohort genomic correlation object and the genomic differentiation object may be the same object.

In some embodiments, a cohort genomic dataset is assigned to only one entity (e.g., device, document, sensor, etc.), while in other embodiments, a community owner assigns one cohort genomic dataset. Note that additional community members may be authorized to clone. For example, a user may have two devices (eg, a desktop and a laptop computer) that are used in connection with work. The community owner can choose to assign identical copies of the genomic datasets to the devices in this scenario. In this way, each device associated with a user may be granted the same access rights with respect to each enclave 602 . In some of these embodiments, each respective device with a cloned genomic dataset still undergoes qualification, authentication, and/or exchange of links with other cohorts within enclave 602. Note that you will have to do this independently.

In some embodiments, when a VDAX is assigned a genomic dataset and added to the digital ecosystem 600, the VDAX participates in the constituent data (e.g., defined in the CG-ESP instance) as well as the ecosystem. It is pointed out that other suitable data may also be received as may be necessary for Such configuration data enables VDAX to use the correct genome features when performing genome manipulations such as eligibility correlation, link spawning, link hosting, sequence mapping, LNA correction, XNA correction, and binary object conversion. can be In these embodiments, such configuration data enables community members to interact and exchange data with other community members. In some embodiments, VDAX may also receive genomic community progeny (GCP) data that uniquely identifies community members. In these embodiments, GCPs may be used in confirming a cohort's eligibility for engagement.

In some embodiments, cohort VDAX 612 may be configured to perform genome security operations and processing on behalf of independent cohort 604 . In some embodiments, cohort VDAX 612 facilitates data exchange with well-correlated community members (eg, other cohorts of enclave 602). In some of these embodiments, facilitating data exchange with another community member includes confirming the eligibility of the engagement (e.g., completeness of engagement and synchronization of engagement); (eg, with another independent cohort 602). In some embodiments, confirmation of eligibility for engagement and link exchange is based on the fact that once the cohort pairs have successfully completed this “handshake”, VDAX pairs are considered to be highly correlated (e.g., identical or other It is a one-time process so that data can be safely exchanged as long as the derivative objects remain shared (sufficiently correlated). For example, as long as a pair of VDAXs first confirms their eligibility for engagement, exchanges links, and no longer shares a common differentiating object, VDAXs can be safe for days, weeks, months, or years. can continue to communicate with Once they no longer share a common differentiating object, they can attempt data exchange, but can no longer decipher any encoded digital objects provided by other respective cohorts.

In embodiments, a pair of VDAXs engages with each other via a Virtual Binary Language Script (VBLS) generated and decoded by each VDAX. As explained, VBLS may refer to a unique, non-recurring (or recurring with infinitesimal probability) binary language. In embodiments, individual instances of VBLS may be referred to as VBLS objects. In embodiments, a first VDAX (e.g., cohort VDAX612 or enclave VDAX610) includes genomic regulatory instructions (GRI) encoded in links provided by a second VDAX to the first VDAX and VBLS objects may be generated for a second VDAX (eg, cohort VDAX612 or enclave VDAX610) based on genomic data (eg, XNA). In these embodiments, the second VDAX may receive the VBLS object from the first VDAX and decode the VBLS based on the GRI and VDAX genomic datasets provided in the link to the first VDAX. may be changed. In some embodiments, the VBLS object includes metadata that the second VDAX processes to decode the encoded digital object contained in the VBLS object. For example, in some embodiments a VBLS object is a data packet that includes a packet header and an encoded digital object (eg, payload). In some of these embodiments, the metadata used to decode the encoded digital object is one or more protocol layers of the digital object (e.g., TCP, UDP, TLS, HTTP, H.256 , or any other suitable protocol layer type).

In embodiments, a first VDAX is provided to a second digital object by determining a genomic engagement factor based on the sequence (e.g., published or unpublished sequence) and the genomic differentiation object of the first VDAX. A VBLS object can be created that corresponds to the digital object that will be different. In an embodiment, a first VDAX modifies its genomic differentiation object according to a GRI provided by a second VDAX at a link provided by a second VDAX, and a digital object (e.g., a protocol or format within the digital object). Data) contains sequences (or values derived therefrom) are mapped to modified genomic differentiation objects to obtain genomic engagement factors. In embodiments, the first VDAX may use computationally complex functions (e.g., cryptographic-based functions, non-cryptographic-based functions, or hybrid functions) to map sequences to modified genomic differentiation objects. good. A first VDAX then encodes a digital object (e.g., a packet payload, a shard of a file, a video or audio frame, or any other suitable type of digital object) using the genomic engagement elements, and encodes the encoded digital You can get an object. In embodiments, the first VDAX utilizes computationally complex functions (eg, encryption functions or disambiguation/XOR functions) to encode digital objects based on genomic engagement factors. The first VDAX can then provide the VBLS object containing the metadata (eg, sequence) and the encoded digital object to the second VDAX (eg, over a network and/or data bus). .

In embodiments, the second VDAX may receive the VBLS object and decode the encoded digital objects within the VBLS object based on the metadata contained in the VBLS object and the genomic identification object of the second VDAX. . In an embodiment, the second VDAX is configured to extract a sequence (eg, an unencrypted portion of a public or private sequence of data packets or data frames) from the VBLS object. The second VDAX also uses the GRI contained in the links provided to the first VDAX to modify its genomic differentiation objects (e.g., during the link exchange process) so that the second VDAX can sequence (or obtained values) can also be mapped to modified genomic differentiation objects using the same computational complexity function so that genomic engagement factors can be obtained. Assuming that the first VDAX and the second VDAX have matching (or in some embodiments sufficiently correlated) genomic differentiation objects, and both use the same instructions to modify their respective genomic differentiation objects. , given the same sequence and modified genomic differentiation object, will produce the same genomic engagement factor. In embodiments, the second VDAX utilizes a function (eg, a decryption function or a disambiguation/XOR function) to decrypt digital objects based on the same genomic engagement factor. In this way, the first VDAX and the second VDAX will not be able to modify the genomic identification object in the same way, because other community members who do not own the link that the second VDAX provided to the first VDAX cannot modify the genomic identification object in the same way. It can be uniquely differentiated from other community members sharing the same genomic identification object. Therefore, other community members would not be able to generate genome engagement factors even if those community members were configured to perform the same computationally complex mapping function and could determine the published sequence. deaf. Intrusive devices or other malicious devices that do not have access to genomic data may find that such intruders use computationally complex functions used to generate genomic engagement factors, methods of extracting sequences, or common genomic differentiation. You may not know one or more of the targets, so you will be further limited. Therefore, genome engagement factors cannot be determined without the brute force method. Additionally, CG-ESP can be configured such that the first VDAX computes new genomic engagement factors for all digital objects (e.g., all data packets, shards of files, video frames, audio frames, etc.). So each encoded VBLS object would require a separate brute-force decision of the digital object, making the VBLS generated by the first VDAX quantum-defensive against the second VDAX.

In some embodiments, VBLS object metadata may further include data consistency information. For example, data integrity information may be a value computed by the first VDAX on plain data and then used as a sequence. Thus, the second VDAX can confirm that the VBLS object has not been tampered with.

In embodiments, digital objects may refer to OSI components (eg, level 2-7 components) and/or computer-executable code/instructions. Examples of digital objects include packets, sectors, frames, and sequences. VBLS may refer to a language spoken by one enclave or cohort to another enclave or cohort that is uniquely understood by a recipient enclave or cohort, i.e. a language understood only by the recipient enclave or cohort. Thus, intruders targeting rogue cohorts, viruses, malware, etc. cannot generate or decrypt VBLS between legitimate cohorts.

It is understood that the foregoing description is provided as an example of a CG-enabled digital ecosystem 600. It is understood that different configurations of CG-ESP may perform different functions and operations and may have different CG-ESP modules. For example, different configurations of CG-ESP can use different encryption functions, different hash functions, different sequence mapping functions, different types of genome construction, or the like. It is further noted that CG-ESP may be configured for different ecosystems, which may enable different architectures.

Figures 7-11 show different types of digital ecosystems and their corresponding architectures. Modern network capabilities substantially reflect their underlying deployment architecture. In embodiments, a CG-enabled architecture that uses genomic structure to enable VBLS can operate at the bit level and thus maintain interoperability with the underlying deployment architecture. VBLS provides unprecedented facilities and flexibility to uniquely tune applications for network, software, and/or hardware-centric architectures. Examples of CG-ESP ecosystem architectures include, but are not limited to: They are architectures that support static ecosystems, free-form architectures that are configured for transitional ecosystems, voluntary architectures that support dynamic ecosystems, transient architectures that support executable ecosystems, and an interledger architecture that supports a positive ecosystem. In embodiments, these architectures, which can overlay existing physical network topologies, demonstrate genome-engineered topologies. In some embodiments, multiple genome assembly topologies can exist simultaneously and interoperably. For example, a computing device may be a viable ecosystem such that internal components of the computing device exchange VBLS, while the computing device uses different sets of genomic data to create a static ecosystem. It may be a member of a static ecosystem that can engage with other devices in the system.

With reference to Figure 7, the characteristics of such ecosystems, and the enclaves and cohorts that participate in these ecosystems, exhibit fairly stable configurations, so directed architectures can be implemented in static ecosystems. can. For example, in enterprise deployments, the majority of users have similar devices (e.g., desktops, laptops, and mobile devices), email clients, software solutions (both cloud-based and running locally), devices (e.g., printers, IoT devices), etc., all of which may not change much over time. These ecosystems offer stable relationships that remain flexible and unhindered. These architectural attributes are uniquely extended and enhanced - so that they can stand on their own. In some exemplary embodiments, a static architecture, as opposed to a free-form architecture, may stipulate how the correlation is performed and managed. For static architectures, correlation is achieved based on common genomic structures provided by a single ecosystem VDAX.

In embodiments, a directed architecture reflects a configuration in which ecosystem VDAX establishes one or more enclaves exhibiting specific genomic correlations and differentiations. In some of these embodiments, each enclave VDAX may correspondingly establish one or more cohorts that also exhibit certain genomic correlations and differentiations. In embodiments, the organization of such ecosystems, enclaves and cohorts may exhibit hierarchical genomic correlation and differentiation, which may be beneficial in directed architectures. In embodiments, directed architectures, ecosystems, enclaves, and cohort VDAX may have multiple genomic correlation and differentiation attributes. For example, an enclave in a directed architecture may propagate both subordinate enclaves and cohorts. In embodiments, different architectures may be configured to exhibit different correlation properties. For example, a directed architecture may exhibit inherently common correlations, and a free-form architecture may exhibit arranged common correlations.

In embodiments, genomic correlations and differentiations allow the construction of genomic network topologies in directional architectures without the need to change the physical topology. For example, in these embodiments, community owners control which LNAs and/or XNAs are provided to different enclave members so that engagement between cohorts in different enclaves (e.g., via link exchanges) is controlled by the community. As can be prevented, different LNAs and XNAs may be used to control cohort relationships in different enclaves. Similarly, in these examples, community owners can also create new enclaves by controlling the LNAs and XNAs served to different cohorts.

In embodiments, the genome topology enabled by the directed architecture may be progressively genomically modified. In some of these embodiments, a community owner may, for any number of considerations (e.g., security, removal of no-longer cohorts, dissolution of enclaves, etc.) The particular genomic organization (eg, XNA and/or LNA) of enclave members may be periodically modified.

FIG. 7 shows an example of a CG-enabled ecosystem 700 with a directional architecture, whereby ecosystem 700 is a static ecosystem. In embodiments, the static ecosystem includes enclaves and cohorts that support more traditional deployments to include local, internally managed but distributed, and remote on-demand IT resources and capabilities. A static ecosystem demands seamless performance and security. Such deployments are common in enterprise-class organizations and institutions, but their complexity makes security a challenge for small and medium-sized businesses (SMBs). These implementations tend to be relatively static and centrally managed. For example, in a static ecosystem, a business unit may contain many employees who are allowed access (e.g., read, write, and/or edit) to a common set of files . In addition, employees may work on special projects (eg, product releases), and these employees are typically granted access to another common set of files. In some scenarios, a community owner (e.g., represented by an IT administrator or any other party affiliated to an enterprise) may decide that individual cohorts have specific files or folders, one or Define multiple enclaves, a set of policies that define the type of access that may be granted for cohorts and/or enclaves (printers, local file servers, etc.) with which each cohort may engage digitally, and/or other appropriate policies You can As explained, such policies may be implemented using genomic constructs such as XNA, CNA, PNA, and LNA, which define permissible relationships and genomic topologies across ecosystems. can be used for

In embodiments, where the digital ecosystem is the static ecosystem 700, the security platform may be implemented as a oriented architecture. Using a oriented architecture, an ecosystem VDAX (eg, private VDAX 702) defines one or more enclaves 704 that correspond to static ecosystems. In the example of FIG. 7, the ecosystem VDAX hierarchically defines N enclaves 704, including a first enclave 704-1 and an Nth enclave 704-N (eg, a directional architecture). . For each enclave 704-1...704-N, the ecosystem VDAX 702 can create an enclave VDAX (running enclave VDAX) for enclave 704-1 and assign one or more cohorts 706 to enclave 704-1. can be assigned. In this example, cohort 706 of first enclave 704-1 and Nth enclave 704-N includes workstations, tablets, local data centers, printers, IoT devices, mobile devices, and the like. Note that in this example the routers in each enclave are not considered cohorts and do not communicate using VBLS. Rather, each router is a pass-through device that routes data packets containing VBLS to cohorts 706 within enclave 704, within ecosystem 700, and/or to any wider network (eg, the Internet). In embodiments where routers are cohorts, each router may have its own genomic dataset (XNA, LNA, and CNA), and other cohorts 706 within the enclave 704 have VBLS that only the router understands. will be used to communicate with the router. It is understood that such decisions are design choices that can be made by community owners or providers of CG-ESPs. Additionally, cloud facility 710 in this example is not an enclave of ecosystem 700. be pointed out. In this example, cloud facility 710 hosts third party applications and/or data. In some exemplary embodiments, ecosystem VDAX 702 of ecosystem 700 uses VDAX (not shown) of third-party application systems and/or cloud facilities to obtain genomic material correlated to the designated ecosystem cohort. ), thereby enabling authentication, connectivity, and engagement between the ecosystem and third-party application systems/cloud facilities. Additionally or alternatively, community owners can add specific third-party service providers (e.g., cloud services) to the ecosystem as cohorts, allowing community owners to integrate third-party service providers via LNA and XNA construction. It can also be decided that access to the ecosystem can be restricted to specific uses. In this way, the third party service could only exchange links with other cohorts with similar LNAs (eg, intended users of the third party service). Similarly, if a relationship with a Third Party Service Provider is terminated, a Community Owner may revoke the Third Party Service Provider through an XNA Amendment.

In some embodiments, the enclave VDAX (or ecosystem VDAX) of enclave 704 can generate and assign genomic material to the VDAX of each cohort 706 of enclave 704 . In embodiments, ecosystem VDAX702 produces genomic information (eg, XNA, LNA, and CNA) for each enclave. In response to receiving that genomic information, enclave VDAX may generate respective genomic information for each cohort 706 included in enclave 704 . For example, an enclave VDAX (or ecosystem VDAX) may generate CNAs to be assigned to new cohorts and/or may provide its LNAs and/or XNAs to enclave 704 members. Depending on the composition of the CG-ESP and its genomic structure, it may be necessary for two cohort VDAXs enrolled in the enclave to participate in the link exchange. Two VDAXs participating in a link exchange can initiate the exchange of VBLS based on the hosted link.

In a oriented architecture, an ecosystem owner (e.g., via ecosystem VDAX 702) may initiate XNA and/or LNA modification of cohort 706 and/or enclave 704, thereby increasing the security of cohort 706 within enclave 704. It is possible to manage functions. For example, if an employee is no longer part of a business unit, that employee's access to certain resources (eg, documents, printers, file systems, etc.) may be revoked. In embodiments, the VDAX may modify the XNA (and in some scenarios LNA ) to “revoke” access to a cohort of employees (eg, workstations, mobile devices, etc.). In another example, an employee has access to the first folder of documents and the second folder of documents, and the employee's access to the first folder is revoked, but the access to the second folder is revoked. access to the second folder is not revoked, VDAX will not provide corrections to the cohort(s) of employees whose access to the second folder has been revoked, and the XNA and enclave DNA and LNA corrections in other cohorts can be undertaken. In these provided examples, community owners can use genome architecture (eg, LNA and/or XNA) to control the engagement of different enclave cohorts.

It is appreciated that the foregoing description provides several exemplary implementations of directional architectures. It is understood that the CG-ESP may be configured according to directed architectures in other suitable ecosystems without departing from the scope of this disclosure.

Referring now to Figure 8, free-form architecture features potentially unrelated ecosystems, enclaves, and cohorts. In such an ecosystem, the composition of the enclave (e.g. a limited number of users, devices, etc.) is fairly stable, but can change according to mutual interests (e.g. changes are less predictable than static ecosystems). Thus, a free-form architecture may provide relationship stability without loosing or inhibiting flexibility. In some embodiments, these architectural attributes may be uniquely extended and enhanced - such an architecture can stand alone. In embodiments, a definition that may be enabled by a free-form architecture, as opposed to a static architecture, is how the correlation is performed and managed. For free-form architectures, common genome correlations cannot be achieved on the same basis as for static architectures, and as such, alternative genome substructures are used to facilitate common genome correlations. It is achieved by using, for example, free-form.

In embodiments, the free-form architecture may facilitate genomic construction of application-specific network topologies. For example, in some embodiments, the ecosystem VDAX independently initiates its own genomic correlation and/or differentiation constructs (eg, LNA and/or XNA). In some embodiments, enclave VDAX and/or cohort VDAX in directed architectures may acquire their unique genomic correlation constructs (eg, LNAs) directly. For example, the ecosystem VDAX may control which cohorts should belong to which enclaves through the generation and modification of genomic constructs.

In embodiments, ecosystem VDAX in free-form architectures may acquire their unique genomic correlation structures through alternative genomic substructures. In some of these embodiments, each ecosystem VDAX may have its own genomic structure from which correlation and differentiation attributes are derived. In some of these embodiments, these derived attributes control the genomic topology of the ecosystem enclave. In some of these embodiments, each enclave (e.g., via its enclave VDAX) may have a unique genome architecture from which correlation and differentiation attributes are derived, and these attributes are derived from cohorts of enclaves ( control the genome topology of the cohort VDAX). In embodiments, multiple genome assembly topologies that overlay physical network topologies may exist simultaneously and in an interoperable manner. In some embodiments, the genome-based digital network topology is independent of the underlying technology used to enable physical or logical digital networks. In these embodiments, the genome-based digital network renders its topology, which in some scenarios can only rely on genome assembly-facilitated VBLS.

FIG. 8 is a diagram illustrating an example of a security platform with a free-form architecture serving an interactive ecosystem 800 . In embodiments, the interactive ecosystem 800 may include one or more enclaves 804 (eg, private residences, home offices, small businesses, etc.), each enclave comprising one or more cohorts 806 ( computers, consumer electronics, hubs, media devices, IoT devices, wearable devices, smart speakers, etc.) should mutually identify things of interest, and services and applications that are interactive but require user-controlled security. It is possible to interact with a wide range of network-enabled web portals (Facebook, Amazon, banking servers, healthcare servers, etc.). An example of an interactive ecosystem 800 may be a home network, small office network, and the like.

In a free-form architecture, a cohort 806 within ecosystem 800 may be designated as VDAX 802 of ecosystem 800 . For example, a user can designate a mobile device, desktop, or router to act as a VDAX 802 for ecosystem 800 . Additionally, through VDAX 802, a user may define one or more enclaves (eg, through a user interface). For example, in some situations a user may define a single enclave 804 (eg, all devices associated with the user). In another example, a user may choose different family members, different device classes, and/or other logical commonalities (e.g., enclaves for devices used by parents, enclaves for devices used by minors, and thermostats). , enclaves for smart devices such as home appliances, televisions, speakers, etc.) may define different enclaves 804 . In embodiments, a user may define one or more settings (eg, rules, policies, blacklists, whitelists, etc.) for enclaves 804 and/or individual devices via VDAX 802 . These parameters may be used in generating genomic data (eg, XNA, LNA, CNA and/or PNA) for enclave 804 or cohort 806. VDAX 802 may generate genomic data for each enclave 804 . In some embodiments, VDAX 802 may also generate genomic data for each cohort (independent and dependent). In other embodiments, another device may host an enclave VDAX, whereby the enclave VDAX generates XNAs, LNAs, PNAs and/or CNAs for cohort 806 within enclave 804 .

In interactive ecosystem 800, the external systems that cohort 806 can access are extensive. For example, users access web portals and stream videos, access social media platforms, visit websites, read emails and messages, and open attachments using workstations or mobile devices. , and so on. Similarly, users may have devices in their homes that can record motion detection, voice recording, video capture, sensor measurements, or other data related to the user or their home or office. These devices can also access web portals to report data or to utilize web portal services (eg, order merchandise, adjust thermostats, etc.). In the former example, users may be concerned about privacy and/or having malicious software (eg, viruses or malware) installed on their devices. In the latter example, users may be concerned about privacy (eg, who has access to data or unknown surveillance captured by their smart device). In the interactive ecosystem 800, a security platform implemented as a free-form architecture alleviates these concerns. In some embodiments, VDAX 802 of ecosystem 800 may negotiate a secure relationship with VDAX of portal 810 (not shown). In some of these embodiments, the user and the web portal's VDAX generate correlated genomic data. In these embodiments, the interactive ecosystem's VDAX 802 may then generate genomic data for cohorts (eg, by the cohort's VDAX) that seek to access the web portal 810 . When cohort 806 attempts to access the web portal, cohort 806 and web portal 810 allow corresponding VDAX pairs to verify eligibility-completeness and/or synchronization and eventually exchange links. Generate and exchange engagement information. Once web portal 810 and cohort 806 have generated and exchanged links, cohort 806 and web portal 810 may each host the other's link. Cohort 806 may use links generated and hosted by web portal 810 to generate VBLS to be sent to web portal 810, and web portal 810 generates VBLS to be sent to cohort 806. For this purpose, the links generated by cohort 806 may be used. The preceding example is an example of a free-form architecture, and other implementations are within the scope of this disclosure.

Referring now to Figure 9, metric (e.g., time, data, conditions, demand, coordinates, behavior, relative position, and events) are used to support applications and services that are subject to highly dynamic changes in state. architecture can be implemented. For example, an autonomous vehicle management system may manage an ecosystem of autonomous vehicles that roam throughout a grid of controls where the grid of controls is dynamically controlled based on traffic conditions on the road. In embodiments, this grid topology may be dynamically reconfigured to enable support for highly dynamic changes in environment and conditions. As a further example, a spontaneous architecture may provide an air traffic control system or a military theater or a swarm of drones whose cohorts are constantly changing and may have highly dynamic security responses to the environment. In embodiments, spontaneous topology can change by altering DNA in response to contextual events. To accommodate these situational events, the altered DNA can be dynamically distributed to different cohorts or groups.

In embodiments, such architectures--autonomous architectures--represent complete and/or real-time reconstructions of their network topology to address specific control parameters such as metric state or operator preference(s). You can benefit from the composition. In certain situations, network architectures are required to meet additional challenges in that they are unable to support highly dynamic changes in the diversity of metrics and their possible states. In embodiments, the spontaneous architecture addresses these highly dynamic changes in metrics enabling support for emerging ultra-bandwidth applications or artificial intelligence portals. Further applications of voluntary architectures include the military arena, power grid management, and highly distributed financial trading systems.

In embodiments, the ecosystem VDAX can build and control genomic network topologies that support applications requiring dynamic state attributes. In embodiments, the ecosystem VDAX uses different genome constructs (e.g., LNA and XNA) so that engagement (e.g., via link exchange) between cohorts within the ecosystem is provided to different members of the LNA and/or the possibility of controlling cohort engagement within the ecosystem such that it can be enabled, blocked, and revoked by community owners by controlling XNA. Similarly, in these examples, the ecosystem VDAX can change the dynamic network topology of the ecosystem by controlling the LNAs and XNAs served to different cohorts.

In embodiments, the enclave VDAX can be configured to control respective portions of the genome network topology of the ecosystem, whereby the enclave VDAX has specific VD AX designation functions for the enclave VDAX's portion of the genome network topology. and responsible for processing. In a further embodiment, genomic correlation and differentiation enable enclave VDAX to construct dynamic genomic network topologies without the need to modify physical topology. For example, in these embodiments, an enclave VDAX can use different genome constructs (e.g., LNA and XNA) to control engagement of cohorts in different enclaves, such engagement (e.g., link exchange) can be dynamically enabled, prevented, or restored by the enclave VDAX by controlling the LNAs and/or XNAs provided to different enclave members. Similarly, in these examples, the enclave VDAX can also create new dynamic spontaneous enclaves by controlling the genomic constructs (eg, LNA and XNA) provided to different cohorts. In some embodiments, cohort VDAX can control their respective parts of the ecosystem genome network topology. In these embodiments, a cohort VDAX may be responsible for performing specific functions on respective portions of the genome network topology specified by the ecosystem VDAX and/or enclave VDAX. In a further embodiment, a cohort VDAX performing such functions may enable spontaneous architecture by constructing a dynamic genomic network topology without the need to modify the physical network topology. For example, in these embodiments, cohort VDAX can use different genomic structures (e.g., LNA and XNA) to control VDAX engagement with specified portions of the genome network topology, and the specified portions Engagement between VDAXs (e.g., by link exchange) in a So it is possible.

In embodiments, various types of interactions (e.g., ecosystem VDAX to enclave VDAX, enclave VDAX to cohort VDAX, ecosystem VDAX to cohort VDAX, and/or cohort VDAX to cohort VDAX interactions) are It may be controlled by determining specific genomic structures (eg, CNA, PNA, LNA and XNA). For example, in these embodiments, the ecosystem VDAX dynamically configures some or all of the ecosystem, enclave, and/or cohort VDAX genome construction (e.g., LNA or XNA) in each portion of the genome network topology. Alterations may control the engagement between VDAXs corresponding to respective parts of the genome network topology. In this way, VDAX can be added, prevented, and/or withdrawn from different parts of the genome network topology through each genome assembly. For example, in some embodiments, enclave VDAXs designated by controlling respective portions of the genome network topology selectively select genomic structures of the VDAX to be added and/or withdrawn from respective portions of the genome network topology. By altering it is possible to change that part of the genome network topology. In embodiments, genomic constructs responsible for ecosystem, enclave, and cohort engagement (e.g., CNA, PNA, LNA and/or XNA) are modified to alter the basis of differentiation and/or correlation, thereby Network topology can be modified. In some embodiments, such corrections may be performed as part of updating the genome network topology (eg, undoing cohorts). In embodiments, these genome structures may be modified to allow controlling the engagement of cohorts in the ecosystem with different genome topologies. Constructs (e.g., LNA and XNA) can enable, prevent, or reverse engagement (e.g., via link exchange) between members within an ecosystem by controlling the genomic construct. do. Similarly, in these examples, the ecosystem CNA, PNA, LNA and/or XNA can alter the ecosystem's dynamic network topology by controlling the genome structure provided to different cohorts.

In embodiments, spontaneous architectures preserve their operational integrity regardless of the dynamic frequency of the metric states they support (e.g., time, data, conditions, requests, coordinates, actions, or events) . For example, in some embodiments, cohorts may operate in environments where the reporting frequency of metrics may be variable. In these embodiments, the voluntary architecture handles these fluctuations in global metric data while maintaining the overall integrity of the ecosystem.

FIG. 9 illustrates an example security platform with a voluntary architecture serving a dynamic ecosystem 900 . In an embodiment, the dynamic ecosystem 900 consists of enclaves and their cohorts that support applications and services that undergo highly dynamic changes in state (time, data, conditions, demand, coordinates, behavior, etc.). include. Examples of dynamic ecosystems 900 include artificial intelligence applications, autonomous vehicle systems, and real-time supply chain systems. The spontaneous ecosystem 900 often requires complete reconfiguration in real time in response to specific conditions and/or operator preference(s). The security requirements of these ecosystems are such that traditional cryptographic protocols cannot support dynamic frequencies and are incompatible with the various states that may be present. In embodiments, the security platform is implemented as a voluntary architecture to provide a voluntary ecosystem 900 . These architectures hold great promise for new integrations of ultra-bandwidth and artificial intelligence (AI) portals.

In a voluntary architecture, ecosystem VDAX 902 may be configured to dynamically define enclaves 904 and/or assign cohorts 906 to one or more enclaves 904 in real time. In some of these embodiments, an AI portal can be leveraged by VDAX 902 to define enclaves 904 and assign cohorts 906 thereto. For each cohort 906 , VDAX 902 may first generate and provide genomic information for cohort 906 . This genomic information may be generated and provided daily, each time cohort 906 is powered up, or at other suitable intervals. Genomic information may be correlated with all other cohorts 906 within the ecosystem 900, but is not assigned to a particular enclave 904. Once a cohort 906 joins the ecosystem 900, the VDAX 902 and AI portal 910 can determine which enclave 904 the cohort 906 belongs to and from which enclave 904 the cohort 906 should be revoked. For each enclave 904 to which a cohort 906 belongs, the VDAX 902 may propagate modifications to the cohort's XNA and LNA so that the AI portal 910 can decipher the VBLS generated by the cohort 906 within those enclaves 904 . Similarly, for each enclave 904 whose cohort 906 has been revoked, VDAX makes changes to the cohort's XNA and LNA so that the cohort 906 can no longer decrypt the VBLS generated for the remaining cohorts 906 within those enclaves 904. may be transmitted. In a voluntary ecosystem, a VDAX (or multiple VDAXes) ensures that cohorts 906 in grid 912 maintain a high level of correlation with other cohorts 906 in enclave 904 and cohorts 906 that are no longer in grid 912 Memberships to enclaves 904 within ecosystem 900 may be managed in this manner such that , no longer maintain a high level of correlation.

In the illustrated example, voluntary ecosystem 900 is an autonomous vehicle environment. In such environments, vehicles may traverse roadways in a region (eg, an entire city, an entire state, etc.). Sometimes there may be hundreds of thousands of vehicles crossing the roadway, and other times there may be fewer vehicles. Each vehicle may be configured to report its sensor data (e.g., LIDAR, radar, video, moisture, etc.) to a cloud-based system, which reports condition data about the roadway (e.g., vehicles, obstacles, etc.). , traffic, etc.). The cloud-based system is configured to report status data associated with each vehicle so as to inform the vehicle of conditions along the vehicle's route (or other suitable data, such as directions to a particular vehicle). may Because each vehicle travels along its own route, the amount of data collected each second from the fleet of vehicles can be enormous. In the illustrated example, the VDAX 902 and AI Portal use VBLS to facilitate reporting relevant status data to vehicles along the grid. In this example, VDAX 902 may generate grids corresponding to areas (eg, cities, counties, states, etc.), and grid 912 has cells. The cells may be fixed in size or dynamically sized according to traffic on the road. Similarly, cells may be fixed in number or dynamically allocated depending on traffic on the road. In some embodiments, the AI Portal will, depending on road conditions (e.g., how many vehicles are on the road, how many vehicles are conventionally on the road at this time, etc.), Determine the number of cells and/or cell size. In embodiments, each cell is considered an enclave 904 and the cloud-based system may report status data related to those vehicles within the enclave 904 . In some embodiments, a communication tower (eg, 5G tower) may host an enclave VDAX that communicates with cohort 906 within enclave 904 . As vehicles cross the roadway, they may exit one cell and enter another. In addition, because the vehicle is likely to go straight, turn right, or turn left, VDAX902 will determine if the vehicle is in one or more cells immediately in front of the vehicle, one or more cells to the right of the vehicle, and one or more cells to the left of the vehicle. A vehicle may be assigned to multiple cells (ie, enclaves) so that it can receive relevant state data for one or more cells. VDAX 902 may provide the vehicle with genomic information about each of these enclaves (eg, one or more cells to the right, left, and front of the vehicle). For each cell/enclave, a vehicle (eg, a cohort VDAX running on it) may generate a GEC and exchange the GEC with a cloud-based system to authenticate itself to a particular cell. Once authenticated, the vehicle and cloud-based system may exchange links to engage with each cell. Once the vehicle collects sensor data, the vehicle can generate a VBLS based on information contained in the collected sensor data, XNA, and links received from cloud-based systems. Similarly, the cloud-based system may broadcast for each cell a VBLS generated with a VBLS specific to that cell (eg, understood by any cohort assigned to the enclave). When a vehicle leaves a cell, VDAX may modify the genetic information of the vehicle corresponding to that cell so that the vehicle no longer understands the VBLS corresponding to that cell, or You may make it impossible to generate.

The application ecosystem continues to evolve, featuring complex services and processes that require greater resource availability and low-latency networks. This is evidenced by the redistribution of processes and the rearrangement of infrastructure. Applications generally require an advanced OS. OS services are becoming increasingly bifurcated: those with low complexity and resource requirements are hosted locally (e.g., client OS), and those with high complexity and resource requirements are hosted remotely (e.g., cloud OS). This efficient OS dichotomy includes the proliferation of very low-cost client devices with access to powerful non-resident features, very low bandwidth budgets, free distribution and development of powerful new applications, among others. also has great advantages. These apparently new and beneficial applications, like their predecessors, will pose significantly more complex challenges to access and proprietary control.

Referring now to Figure 10, by implementing a computationally complex genome structure, CG-ESP integrates different software and hardware components, such as applications (APIs, libraries, and threads), operating systems (kernels, services, drivers). , and libraries), and a way to uniquely transform the relationship between the system-on-chip (processing units, eg, cores). These ecosystem components can jointly or independently sue executable binaries. In embodiments, the method specifically designates and organizes such ecosystems and enclaves and cohorts (independent and dependent cohorts) - which best attend to their capabilities, limitations, and performance efficiencies - It may be possible to form a gnome-constructed temporary architecture. In embodiments, the transient architecture is capable of transforming executable binaries into VBLS digital objects and resulting VBLS streams that exhibit unique genomic differentiation and correlations. In some of these embodiments, CG-ESP in temporal architecture is capable of computationally complex genome assembly that facilitates engagement with other CG-enabled architectures such as:

(Directed states (static and free-form ecosystems) and spontaneous states (dynamic states))
In embodiments, the temporal architecture may offer many advantages in that many of its attributes exhibit direct correlation with other architectures (eg, directed and spontaneous). ), however, transient architectures constitute a very different attack surface in that their components are generally tightly coupled and the process is highly observable and changeable both before and during the process. there is Therefore, dynamic virtual trusted execution domains facilitated by VBLS can be useful under such hypothetical conditions.

In embodiments, genomic correlation and differentiation provides a temporal architecture that can be configured genomically by VDAX to enable viable ecosystems (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and dependent VDAX). enable. In further embodiments, and the temporal architectural base, different hierarchical levels of VDAX provide a viable ecosystem that provides in-depth knowledge of the sources that enable the establishment of trusted components. In a further embodiment, a complex ecosystem such as an autonomous vehicle, or a spacecraft, or a mobile phone, or a web services architecture consists of a vast number of components, each made up of further subcomponents, in this example Each layer of the ecosystem allows building a system of component source knowledge with its own VDAX and genome construction. In further embodiments, each component can perform operations to verify the veracity of the origin and operation of sub-components. In this example, the veracity of operations may be underwritten by a genome-configurable exchange with a trusted provisioning source.

In embodiments, the genomically-constructed application-specific executable ecosystem does not require modification of the underlying architectural embodiment. In further embodiments, the underlying architecture remains unchanged and the executable ecosystem exists as an information overlay that can provide source knowledge. In a further embodiment, knowledge of the source of the components of the temporary architecture may be applied to validate the operational parameters of the executable ecosystem.

In embodiments, the viable ecosystem VDAX can independently initiate unique genome correlation construction. In further embodiments, correlation construction may provide verification of membership of subcomponents within an enclave VDAX. In some embodiments, correlation construction may include LNA (genome correlation), CNA (genome engagement-completeness), and/or PNA (genome engagement-qualification). In this example, these configurations may enable virtual authentication.

In embodiments, a viable ecosystem VDAX can independently initiate its own genomic differentiation construct (eg, ZNA). In some of these embodiments, the executable ecosystem-driven differentiation build determines which component is responsible for a particular operation within the ecosystem and/or determines that the components are isolated. may be applied for In some embodiments, differentiation constructs may include ZNA (genome code separation). In this example, these differentiation constructs may allow virtual affiliation.

In embodiments, a viable ecosystem VDAX may directly obtain its unique genomic correlation structure. In further embodiments, correlation constructs may provide validation of subcomponent assignments within genomic progeny VDAX, such as enclave VDAX or cohort VDAX. In some embodiments, correlation constructs may include LNA (genome correlation), CNA (genome engagement-completeness), and/or PNA (genome engagement-qualification). In this example, these constructs may enable virtual authentication.

In embodiments, viable enclaves VDAX may acquire their own genomic differentiation constructs directly. In further embodiments, viable enclave-driven differentiation architecture may be applied to determine which component is responsible for a particular manipulation, or to determine which component is singular. In some embodiments, differentiation constructs may include ZNAs (genomic code isolation). In this example, these derivative constructions may allow for virtual affiliations.

In embodiments, the executable enclave VDAX can directly acquire its native genome correlation syntax. In further embodiments, correlation constructs may provide validation of subcomponent assignments within a genomic legacy VDAX, such as a dependent VDAX or a cohort VDAX. In some embodiments, correlation constructs may include LNA (genome correlation), CNA (genome engagement-completeness), and/or PNA (genome engagement-qualification). In this example, these constructs may enable virtual authentication.

In embodiments, a viable cohort VDAX can directly acquire its unique genomic correlation syntax. In further embodiments, correlation constructs may provide validation of assignment of subcomponents within a genomic body VDAX, such as dependent VDAX. In some embodiments, correlation constructs may include LNA (genome correlation), CNA (genome engagement-completeness), and/or PNA (genome engagement-qualification). In this example, these constructs may enable virtual authentication.

In embodiments, the viable cohort VDAX may acquire their unique genomic differentiation constructs directly. In a further embodiment, an executable enclave-initiated differentiation construction may be applied to determine which component is responsible for a particular operation or determination that the component is isolated. In some of these embodiments, a viable ecosystem initiated differentiation build determines which component is responsible for determining that a particular operation and/or component within the ecosystem is isolated. may be applied to In some embodiments, differentiation constructs may include ZNA (genome code separation). In this example, these differentiation constructs may allow virtual affiliation.

In embodiments, viable VDAXs (with which unrelated VDAXs engage) may acquire their unique genomic correlation organization through alternative genomic sub-organizations. In further embodiments, correlation constructs may provide validation of the assignment of unrelated components. In some embodiments, correlation constructs may include LNA (genome correlation), CNA (genome engagement-completeness), and/or PNA (genome engagement-qualification). In this example, these configurations may enable virtual authentication.

In embodiments, viable VDAXs (to which unrelated VDAXs engage) may acquire their unique genome differentiation organization through alternative genome subassembly. In further embodiments, executable enclave-initiated differentiation construction may be applied to determine which component is responsible for a particular operation or determine that a component is isolated. In some of these embodiments, a viable ecosystem-driven differentiation build determines which components are responsible for specific operations within the ecosystem and/or determines that components are isolated. may be applied for In some embodiments, the differential construct may comprise a ZNA (genomic code isolation). In this example, these differentiating constructs may enable virtual affiliations.

In embodiments, multiple constructed and viable genome topologies may exist simultaneously. In some embodiments, these multiple genomic topologies may provide genomically efficient operations for different architectural functions. In these exemplary embodiments, the different architectural functions may include source verification, operation verification, or license fee payment verification.

In embodiments, each viable ecosystem has a unique genome architecture from which correlation and differentiation attributes are derived, and these attributes control the topology of that enclave's genome. In some embodiments, each viable enclave has a unique genomic architecture from which correlation and differentiation attributes are derived, whereby these genomic attributes can control the topology of that cohort's genome. In further embodiments, these unique constructs provide differentiation across species, progeny, and siblings.

In embodiments, transient architectures with various genomically-constructed constructs are capable of transforming binary data as VBLS-based digital objects and/or streams. In embodiments, the temporary architecture security platform provides virtual agility. In embodiments, VBLS may refer to a language uniquely understood by a recipient enclave or cohort that an enclave or cohort speaks to another enclave or cohort, i.e. a language understood only by the recipient enclave or cohort. . In this example, intruders targeting rogue cohorts, viruses, malware, etc. cannot generate or decrypt VBLS between legitimate cohorts.

In embodiments, VBLS-transformed binary data can be exchanged and charged with components from two or more different formations that have common genomic correlations and differentiations. In embodiments, the different configurations provide state information for operating parameters of the executable ecosystem. In further embodiments, each of these different configurations is capable of operating according to their state and functional components with knowledge of the associated configuration.

In embodiments, the temporary architecture may have multiple genomically-constructed constructs, with specific components converting executable binaries into Virtual Binary Language Script (VBLS)-based digital objects and/or streams. It is possible that it can be converted. In further embodiments, executable binaries may be converted to VBLS Digital Objects or VBLS Streams, this conversion being achieved by the application of genetically constructed constituent components. In further embodiments, conversion is achieved by applying genome sequence mapping and conversion. In embodiments, sequences are central in computationally transforming digital objects into unique non-recurring genomic engagement elements. In an example, the sequences may be widely heterogeneous, and the sequences may require processing that results in a particular level of entropy. In embodiments, sequence mapping may be compatible with a wide range of protocols and formats, or may begin with pre-existing entropy-indicating objects, which may be manipulated by computationally complex genomic processing and functions. , may be transformed into an object that indicates a particular level of entropy.

In embodiments, a VBLS-transformed executable binary can be exchanged and performed by components from two or more different configurations that have common genomic correlations and differentiations.

In embodiments, within a particular temporary architecture, a component may create a VBLS executable binary (e.g., a proprietary computer applications), which components share common genomic correlations and differentiations. In further embodiments, the particular hardware is part of the genome ecosystem and can apply genome correlation processing to enable processing of VBLS executable binaries.

In embodiments, the transient architecture VDAX resident component is executable (VBLS) such that the converted executable binary can only be correctly processed by another transient architecture VDAX-specific hardware component (e.g., SoC core). Binaries (eg, proprietary computer applications) may be converted and the components share common genomic correlations and derivatives. In a further embodiment, another temporary architecture VDAX may be part of the genome ecosystem.

In embodiments, two or more components transform an executable binary (e.g., a proprietary computer application) based on a unique genomic structure known to another component within a particular temporary architecture. can be done. These transformed binaries are reformed as executable binaries only by one of these components and not by any other component. Modification and prosecution of executable binaries is done in-place. In embodiments, the components are part of the same genomic ecosystem or genomic enclave.

In embodiments, within a particular temporal architecture, a particular component may transform an executable binary (e.g., a proprietary computer application) based on a unique genomic structure, which structure may be translated into other temporal architectures. known to the specific components of the architecture. A converted binary from one architecture may be recreated as a binary executable only by specific components of another architecture. Modification and prosecution of executable binaries is done in-place. In further embodiments, the temporal architectures that share components are part of the same genomic ecosystem or genomic enclave. In a further embodiment, the components sharing the transformed executable binary undergo a genomic link exchange to provide knowledge of the component's source. In a further embodiment, knowledge of the source of the components is used in conjunction with further genome construction to establish trust relationships between the components.

In embodiments, within a particular temporal architecture, a component VDAX may transform an executable binary (eg, a proprietary computer application) based on a particular unique genomic structure, which structure is known only to that component. It is what is done. Such transformed binaries may only be reformed as executable binaries by this particular component and not by any other component. The modification and indictment of executable binaries by this particular component is done in-place. In embodiments, these component-specific transformations apply genomic organization based on genomic data known only to the associated component VDAX. In further embodiments, the converted component is capable of operating in a safe manner such that non-component modifications to the converted binary render the converted executable binary inoperable. .

FIG. 10 is a diagram illustrating an example of a security platform having a temporary architecture for servicing an ecosystem 1000 of executables. As described, the executable ecosystem 100 can be any self-contained ecosystem such as computing devices (eg, servers, mobile devices, personal computers, laptop computers, etc.). In embodiments, the transient architecture provides a framework for cohorts 1006 to create and decrypt VBLS-based isolation of executable code instances, thereby providing a real-time virtual trust execution domain that is not subject to intelligent external observation. is provided. For example, in a computing device, the enclave 1004 may include the computing device's system-on-chip (SoC), the device's operating system, and applications. In this example, an ecosystem, an independent cohort of system-on-chip enclaves 1006, may include processor cores, memory devices (eg, RAM, ROM), and the like. The independent cohort 1006 of the operating system enclave contains the operating system kernel, various drivers (network drivers, file system drivers, print drivers, video drivers, camera drivers, dependent "surrogate" cohorts, etc.), shared libraries, and so on. But it's okay. In an example, a dependent cohort of applications 1008 may include threads, APIs, files, and the like. In some embodiments, each enclave (SoC, operating system, applications) is assigned a genomic dataset (e.g., ZNA, LNA, CNA, and/or PNA), which is the May be inherited by cohort. In embodiments, the ecosystem VDAX may create a transient enclave when an application is accessed, whereby the transient enclave consists of a cohort of applications, a cohort of operating systems engaged by the applications, and a cohort of operating systems engaged by the applications. is created for a cohort of SoCs called by the operating system when running In this example, cohorts within a temporary enclave can authenticate each other, exchange links, and generate VBLS. During execution, certain threads of an application may request resources from the operating system kernel. When a thread executes, a separate cohort VDAX representing application threads generates a VBLS based on the executable code of the thread that requests kernel resources. In this scenario, application threads are authenticated by an independent cohort VDAX representing the kernel (e.g., kernel VDAX) using ecosystem genome progeny data generated using the ecosystem CNA assigned to the thread application. (and vice versa). In response, the kernel VDAX and the thread VDAX representing the thread exchange links created using the respective LNAs of the kernel and application threads. A thread VDAX can generate a VBLS based on the executing code instance requesting the resource, the ZNA assigned to the application thread, and the link provided by the kernel VDAX. Thread VDAX provides VBLS to kernel VDAX, kernel VDAX decrypts VBLS. Kernel VDAX serves requested resources (e.g., camera drivers accessing computing device cameras, subordinate "surrogate" cohorts) using VBLS that can only be deciphered by the kernel or the requested resource. can interface with any VDAX.

In some embodiments, a dependent application (as opposed to an independent application) is not capable of safe VBLS isolation of its internal API and thread components. However, both independent and dependent applications are capable of secure VBLS interprocess communication with each other and with authenticated external resources (eg, operating system, system on chip). In these embodiments, temporary enclaves enable secure VBLS isolation of kernels and processing cores, ensuring that all digital objects on the system bus are only engaged by specific applications, operating systems, and/or SoC cohorts. guarantee to do so.

FIG. 11 is a diagram illustrating an example set of CG-based operations of a process for facilitating VBLS-based data exchange performed by a set of members within a CG-enabled ecosystem. The processes, modules, and techniques described with respect to FIG. 11 are provided as exemplary implementations of CG-based operations that may be performed by a VDAX executing a particular configuration of CG-ESP, and are intended to limit the scope of this disclosure. Not intended. It will be appreciated that different CG-ESPs may be configured to perform different CG-based operations and implement VBLS-based data exchanges accordingly.

In an example, an ancestor VDAX 1102 (e.g., an ecosystem VDAX or an enclave VDAX) digitally generates respective genomic data sets (DNA) for a set of descendants VDAX 1104-1, 1104-2, ... may be configured to add X community members (eg, independent cohorts) to a community (eg, an enclave). 1104-X, each corresponding to X community members, includes a first descendant VDAX 1104-1 and a second descendant VDAX 1104-2. In an embodiment, ancestor VDAX 1102 and descendants VDAX 1104-1, 1104-2, . 1104-X runs each CG-ESP instance, including root DNA module, link module, sequence mapping module, and binary conversion module (eg, as described with respect to FIG. 4). It will be appreciated that a CG-ESP instance may include additional and/or alternative modules without departing from the scope of this disclosure.

In some embodiments, the root DNA module of an ancestral VDAX 1102 may digitally generate an ancestral genome dataset or be assigned an ancestral genome dataset from another VDAX. In embodiments, the root DNA module of an ancestral VDAX 1102 digitally generates a respective progeny genome dataset for each progeny VDAX 1104 within a digital community (e.g., an ecosystem or enclave) and distributes each progeny genome dataset to each progeny VDAX 1104 assign to In some of these embodiments, the progenitor VDAX 1102 uses a set of information-theoretically-enhanced computational complexity functions to digitally generate each progeny genome dataset from the progenitor genome dataset assigned to the progenitor VDAX 1102. may be generated in

In embodiments, the ancestral VDAX1102 genomic dataset includes an ancestral genome eligibility object (e.g., CNA and/or PNA), an ancestral genome correlation object (e.g., LNA), and/or an ancestral genome differentiation object (e.g., XNA or ZNA ) may be included. In some of these embodiments, the progenitor VDAX 1102 was highly correlated, assuming all of the progeny VDAX 1104 were able to communicate (e.g., the progeny VDAX shared mutual identities of interest). Genomic correlation and differentiation objects (eg, identical or otherwise sufficiently correlated) may be assigned to each progeny VDAX 1104 . In some embodiments, the ancestral VDAX 1102 may use a set of information-theoretically-enhanced computationally complex functions to generate unique but highly correlated genomic eligibility objects.

In embodiments, the ancestral VDAX1102 genomic data set is provided to the ancestral VDAX1102 in a one-time "trusted" event. For example, the progeny genomic data may be transferred on a USB stick or other connectable physical medium via wired communication between the progenitor VDAX 1102 and the progeny VDAX 1104 (e.g., the physical digital communication port of the device running the progenitor VDAX 1102). , via proximity-based wireless protocols (e.g., near-field communication), via VBLS generated and decoded using different genomic datasets when the device hosting the offspring VDAX1104 is first manufactured or configured VDAX 1102 may be provided to the device running the ancestor VDAX 1102 via the VDAX 1102 and/or via these likes. Once a progeny VDAX 1104 is provided with its progeny genome dataset for a particular community, the progeny VDAX 1104 can engage with any other progeny VDAX 1104 within the community via their respective progeny genome datasets. In the illustrated example, a first descendant VDAX 1104-1 and a second descendant VDAX 1104-2 engage in a series of processes to authenticate each other as community members and facilitate further data exchange as described below.

At 1110, the first VDAX 1104-1 and the second VDAX 1104-2 exchange their respective eligibility information. In embodiments, the exchange of eligibility information may be an asynchronous exchange. In some embodiments, eligibility information may be any suitable data uniquely associated with each community member.

At 1120, the first VDAX 1104-1 root DNA module and the second VDAX 1104-2 root DNA module each use an information-theoretically-enhanced set of computational complexity functions to analyze its genomic data and other Commonality with each other VDAX 1104 is determined based on the eligibility information received from the VDAX 1104 . Since the eligibility information provided by each respective VDAX is unique, the provided eligibility information exchanged by the first VDAX 1104-1 and the second VDAX 1104-2 is asymmetric. However, each VDAX 1104 uses its own genomic datasets (e.g., genomic eligibility objects) and the received eligibility information to determine uniqueness between the first VDAX 1104-1 and second VDAX 1104-2 genomic datasets One can independently determine the commonality (eg, engagement consistency vector) between pairs of VDAX that reflects the correlation of .

At 1130, one or both link modules of VDAX 1104 establish their respective links based on their genomic datasets and sets of genomic regulation instructions (GRIs) specifically determined for data exchange with other VDAXs. produce. As described, the links generate VBLS where each link (e.g., a first link and a second link) is generated by a link-spawn VDAX for a link-hosting VDAX and can be decoded by a spawn VDAX. There may be a mechanism to transport and decode the GRI, such as instructing the hosting VDAX on how. In this way the link is bisymmetrical. In an embodiment, the first VDAX 1104-1 may generate the first GRI and encode the GRI on the first link provided to the second VDAX 1104-2. Similarly, the second VDAX 1104-2 may generate a second GRI and include the second GRI in a second link provided to the first VDAX 1104-1. In embodiments, the link includes a genome engagement cargo (GEC) containing encoded GRI and information used to decode the encoded GRI. In some embodiments, the information used to decode the GRI includes the unencoded sequences and encoded instructions used to modify the genome correlation object. In these embodiments, encoded instructions may be encoded based on communalities (eg, as determined at 1120).

In embodiments, VDAX 1104 randomly generates each GRI and encodes the generation with each link engagement factor. For example, a first VDAX1104-1 may generate a first GRI and use a set of information-theoretically facilitated computational complexity functions to generate the first GRI using the link engagement factor. may be encoded. Additionally or alternatively, a second VDAX1104-2 may generate a second GRI, using a second link engagement factor using a set of information theory-enhanced computational complexity functions to encode the second GRI. In embodiments, the VDAX 1104 generates GRI randomly (eg, randomly generated values) or in some other suitable manner. In embodiments, VDAX 1104 generates link engagement factors by determining correction instructions (e.g., randomly), and then sequences of information to obtain a corrected genomic correlation object (e.g., a corrected LNA). Correct the genomic correlation object based on the correction instructions using a theoretically accelerated computational complexity function. In embodiments, the VDAX 1104 may then generate a sequence (e.g., a public sequence, a private sequence, or any other suitable sequence) to obtain a link engagement factor using information theory-driven A set of computational complex functions may be used to map sequences to modified genomic correlation objects. VDAX 1104 may then encode the GRI using the determined link engagement factor using a set of information-theoretically expedited computational complexity functions to obtain the encoded GRI. In embodiments, the VDAX 1104 may also encode the modification instructions with the communalities (eg, determined at 1120) to generate an encoded GRI, an encoded modification instruction, and a link engagement factor. A GEC may be generated that contains the sequence (leaving the sequence unencoded) used to

In some embodiments, the first VDAX 1104-1 may encode GRI using a first link engagement factor and a set of information-theoretically-enhanced computational complexity functions.

At 1140, the first VDAX 1104-1 and the second VDAX 1104-2 exchange links. In embodiments, the link exchange is performed asynchronously. Assuming both VDAXs have created their respective links, each VDAX 1104-1, 1104-2 may provide the created links to each other VDAX. For example, a first VDAX 1104-1 may provide a first link including a first GEC to a second VDAX 1104-2, and/or the second VDAX 1104-2 may A second link may be provided to the first VDAX 1104-1, which includes the GEC of . In embodiments, the link may include additional information (eg, metadata indicating the source of the link, application-specific metadata, and/or the like) in addition to the genome engagement cargo. VDAX may exchange links in any suitable manner. For example, links may be communicated via a communication network and/or a data bus. Alternatively, the link may be communicated via a physical storage medium (e.g. USB memory drive, CD, DVD, etc.) or via a proximity-based protocol (e.g. NFC or Bluetooth). .

At 1150, the link module of each VDAX 1104 that receives links from other VDAXs 1104 decodes the encoded GRI contained in the received links. For example, the second VDAX 1104-2 may receive the first link from the first VDAX 1104-1 and decode the first genomic instructions from that link. In an exemplary embodiment, the second VDAX 1104-2 uses a set of information theory-enhanced computational complexity functions to determine commonality with the first VDAX 1104-2 (e.g., engagement consistency vector ) may be decoded first. A second VDAX 1104-2 then modifies its genomic data (e.g., its genome correlation object) based on the decrypted modification instructions and generates the modified genomic data (e.g., its modified genome correlation object) may be obtained. Assuming both VDAXs are of the same community, the second VDAX 1104-2 modifies its genomic data (e.g., genomic correlation objects) in the same way that the first VDAX 1104-1 modified its genomic data. , so that the corrected genomic data (eg, corrected correlation objects) are sufficiently correlated. The second VDAX1104-1 then maps the sequences contained in the first GEC to the corrected genomic data (e.g., to the corrected correlation objects) to obtain the first link engagement factor. good. A second VDAX 1104-2 may then decode the first encoded GRI using the first link engagement factor to obtain a first decoded GRI (dGRI). The first GRI is then transferred to the first VDAX 1104-1, such that the second VDAX 1104-2 is said to host the first link, for future engagement with the first VDAX 1104-1. May be stored instead of 1. Similarly, the first VDAX 1104-1 may decode the second link based on the contents of the second GEC to obtain a second decoded GRI, and the second VDAX 1104-2 may A second decoded GRI may be stored instead. Note that link decoding requires that VDAX1104 have highly correlated (eg, identical or otherwise well-correlated) genomic data and have functionally identical organization. If any of these conditions are not met, VDAXs 1104 will not be able to successfully decode the respective link. In embodiments, the link-hosting VDAX is also used to ensure that the spawning links have highly correlated genomic data and functionally identical organization (eg, thereby qualifying the spawning VDAX as an engagable cohort). Other VDAX based links can be verified/authenticated by performing a predetermined set of operations.

Operations 1160-1190 describe operations that may be performed to generate a VBLS after the first and second VDAXs have exchanged links. For purposes of illustration, operations 1160-1190 describe a scenario in which a first VDAX 1104-1 sends VBLS to a second VDAX 1104-2 and the second VDAX 1104-2 decodes the VBLS.

At 1160, the root DNA module of the first VDAX 1104-1 is extracted from the second VDAX 1104-2 using a set of information theory-enhanced computational complexity functions to obtain corrected genomic data. The genomic data (eg, genomic differentiation objects) can be modified based on the provided second GRI. In some embodiments, the first VDAX 1104-1 modifies its XNA or ZNA based on the second GRI to obtain modified XNA or modified ZNA.

At 1170, the sequence mapping module of the first VDAX 1104-1 maps the sequences (e.g., published or unpublished sequences) to its modified genome data set (using the second genome adjustment instructions received from the second VDAX 1104-2). modified results) to obtain the first VBLS genome coefficients. In some embodiments, the first VDAX 1104-1 can obtain the sequence from the first portion of the digital object to be provided to the second VDAX 1102-2. For example, if the digital object is a data packet, the first part of the data packet contains protocol-specific information used to transmit the data packet (e.g., TCP header, UDP header, or other suitable protocol data ) may be included. In an embodiment, the first VDAX1104-1 then uses a set of information-theoretically-enhanced computational complexity functions to map the sequences to the modified genomic data (e.g., modified XNA) to perform genomic engagement You may get a factor.

At 1175, the first VDAX 1104-1 may generate VBLS objects for the second VDAX 1104-2 based on the digital objects and the genomic engagement factors. In embodiments, the binary conversion module of the first VDAX 1104-1 may encode the second portion of the digital object using a set of genomic engagement factors and information theory-enhanced computational complexity functions. In embodiments, the second portion of the digital object may be encoded by disambiguation in which the second portion of the digital object is XORed with the genomic engagement factor to obtain the encoded digital object. Alternatively, the binary conversion module of the first VDAX 1104-1 may encrypt the second portion of the digital object using the genomic engagement factor and the encryption function to obtain the encoded digital object.

At 1180, the first VDAX 1104-1 may provide the VBLS object to the second VDAX 1104-2. In embodiments, the VBLS object contains the encoded digital object, metadata to be used by the first VDAX 1104-1 to decode the encoded digital object, and any other suitable data ( protocol data). For example, in some embodiments, a VBLS object may include a first portion of a digital object (including an extracted sequence and other suitable protocol data) and an encoded digital object.

At 1190, the second VDAX 1104-2 downloads its own genome dataset, the second GRI (provided and decoded to the first VDAX 1104-1), and the metadata contained in each VBLS object. Decode the VBLS provided by the first VD AX 1104-1 based on . In some implementations, a second VDAX1104-2 root DNA module uses a second GRI and a set of information-theoretically-enhanced computationally complex functions to transform its genomic data (e.g., genomic differentiation objects ) to obtain modified genomic data (eg, modified genomic differentiation objects). The sequence mapping module of the second VDAX1104-2 may then extract the sequences from the VBLS objects (e.g., from the first portion of the VBLS objects) and convert the sequences to modified genomic data (e.g., modified genome differentiation objects). ) to obtain genomic engagement factors using a set of information theory-facilitated computational complexity functions. A second VDAX 1104-2 binary conversion module may then decode the encoded digital object based on a set of genomic engagement factors and information theory-enhanced computational complexity functions. For example, the second VDAX1104-2 uses the genome engagement coefficient to disambiguate the encoded digital object and decode the encoded digital object to obtain the decoded digital object. may

In some example implementations, a VBLS object may include validation data provided from a sending VDAX (e.g., first VDAX 1104-1) to a receiving VDAX (e.g., second VDAX 1104-2). , where the validation data are the values used to determine the genome engagement factor used to encode the VBLS object. In these implementations, the receiving VDAX may, after decoding the VBLS object, recompute the validation data using the same set of information-theoretically-enhanced computational complexity functions. If the recomputed validation data matches the validation data contained in the VBLS object, the receiving VDAX knows that the VBLS object was properly decrypted and/or that the VBLS object has not been tampered with by a third party. can be confirmed.

VDAX 1104 can continue to exchange VBLS in this manner. In embodiments, the sending VDAX 1104 may generate new genomic coefficients for each digital object such that each digital object is encoded using unique non-recurring genomic coefficients. Thus, the transmitting VDAX 1104 may iterate between blocks 1170 and 1180 and the receiving VDAX may decode the VBLS. During data exchange, the sending VDAX may have to modify the genomic data if the modified genomic data is no longer valid (e.g., no longer cached or in memory). Please note. In this scenario, the sending VDAX may repeat block 1160 as well. Furthermore, the set described herein can be applied to a variety of protocol stacks (e.g., networking stacks, application stacks, or software stacks), as the sequence mapping technique can be applied to any suitable protocol or format. It can be applied at levels, thereby providing virtual agility.

Note also that blocks 1110-1150 need only be performed once between pairs of well-related VDAXs 1104 (eg, cohort pairs in an enclave). Once the links are exchanged, the VDAX pair 1104 can continue to exchange VBLS even if their respective genome datasets are modified (eg, by progeny VDAX 1102). If one of the community members that VDAX1104 corresponds to is "withdrawn" from the community, its genomic data will be removed from the genome dataset of the other community member due to asymmetric updates to the genome dataset of the withdrawn community member. The set may be updated so that it no longer correlates well with community members that have not been revoked. In this way, a pair of VDAX1104 can no longer modify their genomic datasets in the same way, even though both VDAX1104 still host valid links. Conversely, even if the progeny VDAX1102 updated the genomic datasets of both VDAXs in the same way, the VDAX1104 pair would have the same links, as it would result in GRI making the same modifications to each updated genomic dataset. can be used to continue generating VBLS. As such, link exchange is sometimes referred to as a "one-time process" even though a pair of cohorts may later exchange new links. Additionally, in some implementations, VDAX 1104 pairs may periodically update their links. However, such updates to links do not require execution of the processes at 1140 and 1150; rather, a pair of VDAXs 1104 may encode updated genomic adjustment instructions in their respective VBLS objects, and the updated Note that genome adjustment instructions may be decoded.

Furthermore, it should be noted that the implementation described in FIG. 11 is a non-limiting example implementation of CG-ESP. For example, genomic data assignments, authentications, and link exchanges between two VDAX1104s were significantly correlated from other well-correlated VDAXes where VDAX1104 was 04 via a covert genome regulatory mandate. It may be performed in other suitable manners, provided that the genomic data sets can be sufficiently distinguished. Further, in some example implementations, the captive genomic regulatory orders may be invalid and/or selectively updated. In this way, regardless of the method of initial genomic data allocation, VDAX authentication, or VDAX link exchange, community owners configure CG-ESP to enable one-time authentication and/or link exchange. You can choose to do it selectively. In these implementations, well-correlated VDAX pairs 1104 may be allowed to continue to securely exchange data as long as they have sufficiently correlated and sufficiently differentiable genomic datasets. This kind of flexibility allows community owners to configure CG-ESPs with diverse architectures and configurations to serve diverse ecosystems and community types.

Figures 12 and 13 illustrate additional non-limiting examples of CG-enabled digital ecosystems that may be implemented using the teachings of this disclosure. As can be appreciated, multiple genomic network topologies can be implemented at various levels of these digital ecosystems.

While only certain embodiments of the disclosure have been shown and described, it will be appreciated that many changes and modifications can be made thereto without departing from the spirit and scope of the disclosure as set forth in the following claims. clear to the trader. All patent applications and patents (both foreign and domestic) and all other publications referenced herein are hereby incorporated in their entirety to the fullest extent permitted by law.

The methods and systems described herein may be deployed in part or in whole through machines executing computer software, program code and/or instructions on processors. The present disclosure may be embodied as a method on a machine, as a system or apparatus as part of or associated with a machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines. good too. In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. Processors include central processing units (CPUs), general purpose processing units (GPUs), logic boards, chips (e.g. graphics chips, video processing chips, data compression chips, etc.), chipsets, controllers, system-on-chips (e.g., RF system-on-chip, AI system-on-chip, video processing system-on-chip, etc.), integrated circuits, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), approximate computation processors, quantum computation processors, parallel computation processors, neural A network processor, or other type of processor. A processor is a signal processor, digital processor, data processor, embedded processor, microprocessor, or coprocessor that can directly or indirectly facilitate the execution of program code or program instructions stored thereon (mathematical coprocessor, graphics co-processor, communication co-processor, video co-processor, AI co-processor, etc.). Further, a processor may enable execution of multiple programs, threads, and code. Threads may be executed concurrently to improve processor performance and facilitate concurrent operation of applications. As an implementation, the methods, program code, program instructions, etc. described herein may be implemented in one or more threads. Threads may spawn other threads that may have assigned priorities associated with them, and the processor may assign priority based on instructions provided in the program code or based on any other order. These threads may run. A processor, or any machine that utilizes it, may include non-transitory memory that stores the methods, code, instructions, and programs described herein and elsewhere. A processor may access non-transitory storage media through an interface that may store methods, code, and instructions such as those described herein and elsewhere. Storage media associated with processors for storing methods, programs, codes, program instructions or other types of instructions executable by a computing or processing device include CD-ROMs, DVDs, memory, hard disks, flash drives, RAM , ROM, cache, network attached storage, server-based storage, etc., but may not be limited thereto.

A processor may include one or more cores that can increase the speed and performance of multiprocessors. In embodiments, a process may be a dual-core processor, quad-core processor, other chip-level multiprocessor, etc. that combines two or more independent cores (sometimes called dies).

The methods and systems described herein can be used on servers, clients, firewalls, gateways, hubs, routers, switches, infrastructure-as-a-service, platforms-as-a-service, or other such computers. and/or may be deployed in part or in whole via machines running computer software on networking hardware or systems. The software includes file servers, print servers, domain servers, internet servers, intranet servers, cloud servers, infrastructure-as-service servers, platform-as-service servers, web servers and secondary servers, host servers, distributed servers, fail It may also be associated with servers that may include other variations such as overservers, backup servers, server farms, and the like. Servers are among the interfaces that allow access to other servers, clients, machines, and devices through memory, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and wired or wireless media. May contain one or more. A method, program, or code described herein may be executed by a server. Additionally, other devices required for performing methods as described herein may be considered part of the infrastructure associated with the server.

Servers may provide interfaces to other devices, including clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of programs over a network. Networking some or all of these devices may facilitate parallel processing of a program or method at one or more locations without departing from the scope of this disclosure. Additionally, any of the devices connected to the server via an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may serve as a storage medium for program code, instructions, and programs.

Software programs may be associated with clients, which may include file clients, print clients, domain clients, Internet clients, intranet clients, and other variations such as secondary clients, host clients, distributed clients, and the like. A client may include memory, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and interfaces that may access other clients, servers, machines, and devices through wired or wireless media. may include one or more of Any method, program, or code described herein may be executed by a client. In addition, other equipment required for execution of methods as described herein can be considered part of the infrastructure associated with the client.

Clients may provide interfaces to other devices, including servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and/or connection may facilitate remote execution of programs over a network. Networking some or all of these devices may facilitate parallel processing of a program or method at one or more locations without departing from the scope of this disclosure. Additionally, any of the devices attached to the client via an interface may include at least one storage medium capable of storing methods, programs, applications, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may serve as a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed, in part or in whole, over a network infrastructure. A network infrastructure may include computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices, and other active and passive devices, modules and/or components known in the art. can contain elements. Computing and/or non-computing device(s) associated with the network infrastructure may include storage media such as flash memory, buffers, stacks, RAM, ROM, etc., among other components. The processes, methods, program code, instructions described herein and elsewhere may be performed by one or more of the network infrastructure elements. The methods and systems described herein can be of any type, including those involving software as a service (SaaS), platform as a service (PaaS), and/or infrastructure as a service (IaaS) characteristics. may be adapted for use with any private, community, or hybrid cloud computing network or cloud computing environment.

The methods, program codes, and instructions described herein and elsewhere may be implemented on a cellular network having multiple cells. A cellular network may be either a frequency division multiple access (FDMA) network or a code division multiple access (CDMA) network. A cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like. Cellular networks may be GSM, GPRS, 3G, 4G, 5G, LTE, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein and elsewhere can be implemented on or through mobile devices. Mobile devices may include navigation devices, cell phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, e-book readers, music players, and the like. These devices may include, among other components, storage media such as flash memory, buffers, RAM, ROM, and one or more computing devices. A computing device associated with the mobile device may be capable of executing program codes, methods, and instructions stored thereon. Alternatively, mobile devices may be configured to execute instructions in cooperation with other devices. Mobile devices may communicate with a base station interfaced with a server and configured to execute program code. A mobile device may communicate over a peer-to-peer network, mesh network, or other communication network. The program code may be stored in storage media associated with the servers and executed by computing devices embedded within the servers. A base station may include a computing device and a storage medium. A storage medium may store program code and instructions that are executed by a computing device associated with the base station.

Computer software, program code, and/or instructions are computer components, devices, and recording media that hold digital data used in computing for intervals, semiconductor storage known as random access memory (RAM), optical discs, optical discs, and so on. Forms of magnetic storage such as hard disks, tapes, drums, cards, processor registers, cache memory, volatile memory, non-volatile memory, optical storage such as CDs, DVDs, removable media such as flash memory (e.g. USB stick or key), floppy Removable media such as disks, magnetic tapes, paper tapes, punched cards, standalone RAM disks, Zip drives, removable mass storage, offline; dynamic memory, static memory, read/write storage, variable storage, read-only, random access, sequential Access, Location Addressable, File Addressable, Content Addressable, Network Attached Storage, Storage Area Network, Barcode, Magnetic Ink, Network Attached Storage, Network Storage, NVME Accessible Storage, PCIE Attached Storage, Distributed Storage, etc. Such as computer memory.

The methods and systems described herein can transform physical and/or intangible items from one state to another. The methods and systems described herein can also transform data representing physical and/or intangible items from one state to another.

Elements described and depicted herein, including flowcharts and block diagrams in the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practice, the depicted elements and their functionality may be represented as monolithic software structures, as stand-alone software modules, or as modules employing external routines, code, services, etc., or any of these. and all such implementations may be within the scope of the present disclosure. Examples of such machines include personal digital assistants, laptops, personal computers, mobile phones, other portable computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic Books, gadgets, electronic devices, devices, artificial intelligence, computing devices, network appliances, servers, routers, etc. may include, but are not limited to. Moreover, the elements depicted in flowcharts, block diagrams, or other logical components can be implemented by machines capable of executing program instructions. Thus, while the foregoing drawings and description illustrate functional aspects of the disclosed system, the specific arrangement of software for implementing those functional aspects may be either explicitly described or given in context. No inference should be drawn from these statements unless it is clear from Likewise, it is understood that the various steps identified and described above may be varied and the order of steps may be adapted to suit a particular application of the techniques disclosed herein. Will. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and/or description of an order for various steps does not imply a particular order of execution for those steps unless required by a particular application or explicitly stated or apparent from context. should not understand.

The methods and/or processes described above, and the steps associated therewith, may be implemented in hardware, software, or any combination of hardware and software suitable for a particular application. The hardware may include general purpose computers and/or special purpose computing devices, or specific computing devices, or specific aspects or components of specific computing devices. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, along with internal and/or external memory. The process may also, or alternatively, be embodied in an application specific integrated circuit, programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. Additionally, it will be appreciated that one or more of the processes may be embodied as computer-executable code executable on a machine-readable medium.

Computer-executable code may be written in a structured programming language such as C, an object-oriented programming language such as C++, or any one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or different hardware and software combinations or programs. written using any other high-level or low-level programming language (including assembly language, hardware description languages, database programming languages and techniques) that can be stored, compiled or interpreted for execution on any other machine capable of executing instructions; can do. Computer software may employ virtualization, virtual machines, containers, dock facilities, porters, and other features.

Thus, in one aspect, the methods and combinations thereof described above may be embodied in computer-executable code that performs the steps when executed on one or more computing devices. In another aspect, the method may be embodied in a system that performs its steps, may be distributed among devices in any number of ways, or all of the functionality may reside on a dedicated stand-alone device or other hardware device. may be integrated into In another aspect, means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of this disclosure.

Although the present disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereto will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure are not to be limited by the foregoing examples, but are to be understood in the broadest sense permitted by law.

The use of the singular in the context of describing the present disclosure (particularly in the context of the claims below) shall include both the singular and the plural unless otherwise indicated herein or otherwise clearly contradicted by context. shall be construed as The terms “consisting of,” “having,” “including,” and “including” shall be construed as open-ended terms (i.e., meaning “including but not limited to”) unless otherwise specified. shall be Recitation of ranges of values herein is merely intended to serve as a shorthand method for referring individually to each separate value falling within the range, unless otherwise indicated herein; Values are incorporated herein as if individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples or exemplary language (e.g., "such as") provided herein is merely intended to better illuminate the present disclosure and unless otherwise claimed, is not intended to limit the scope of The term "set" can include a set having a single member. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.

While the foregoing written descriptions enable those skilled in the art to make and use what is presently considered to be the best mode thereof, those skilled in the art will readily recognize variations, modifications, and variations of the specific embodiments, methods, and examples herein. Will understand and understand the existence of combinations and equivalents. Accordingly, the present disclosure should not be limited by the above-described embodiments, methods, and examples, but should be limited by all embodiments and methods within the scope and spirit of the present disclosure.

All documents referenced herein are hereby incorporated by reference as if fully set forth herein.

Claims (164)

An ecosystem security platform executed by a VDAX processing system, the ecosystem security platform comprising:
Manage digitally generated genomic datasets assigned to VDAX, genomic datasets are specific to VDAX and contain a genome eligibility object, a genome correlation object, and a genome differentiation object, one or more computationally complex a root DNA module configured to modify a genomic dataset using a function;
receiving a link from the second VDAX, the link comprising encoded genome regulation instructions;
a linking module configured to decode the link based on the genome eligibility object and the modified genome correlation object to obtain a decoded genome regulation instruction, the modified genome correlation object being the genome correlation object by the root DNA module; a link module, modified from
obtaining a sequence from a digital object to be provided to a second VDAX, extracting the sequence from the first portion of the digital object, mapping the sequence to a modified genome differentiation object to obtain a genome engagement factor, and obtaining a genome engagement factor; a sequence mapping module configured such that the differentiation object is modified from the genomic differentiation object by the root DNA module based on the decoded genomic regulation instructions;
A virtual binary language script (VBLS) configured to encode a second portion of the digital object based on the genomic engagement factor to obtain an encoded digital object, the virtual binary language script (VBLS) comprising the encoded digital object and the first portion of the digital object. a binary conversion module that produces an object; and
The ecosystem security platform, wherein the VBLS object is provided to the second VDAX as part of a series of VBLS objects. 2. The ecosystem security platform of claim 1, wherein the digital objects are part of a series of digital objects each encoded in a series of respective VBLS objects. 3. The ecosystem security platform of Claim 2, wherein each digital object is encoded with a different respective genomic engagement factor. A sequence mapping module obtains each sequence from each digital object, maps each sequence to a modified genomic differentiation object, and obtains each genomic engagement factor used to encode each digital object. The ecosystem security platform of claim 3, wherein: The ecosystem security platform of claim 1, wherein said set of VBLS objects is a non-recursive language native to said VDAX and said second VDAX. 2. The ecosystem security platform of claim 1, wherein said sequence is a public sequence defined in the first part of said digital object according to known protocols and formats. The ecosystem security platform of Claim 1, wherein said sequence is a private sequence defined in said first portion of said digital object according to a proprietary protocol and format not publicly available. The binary conversion module encodes the second portion of the digital object based on the genomic engagement factor by determining a separate sum of the second portion and the genomic engagement factor using an XOR operation. 2. The ecosystem security platform of claim 1, comprising a disambiguation module that The binary conversion module includes an encryption module that encrypts a second portion of the digital object based on the genome engagement factor by encrypting the second part of the digital object with the encryption function and the genome engagement factor. The ecosystem security platform of Section 1. 3. The ecosystem security platform of Claim 1, wherein the link module decodes the link from the second VDAX as part of a link exchange process with the second VDAX. 11. The ecosystem security platform of claim 10, wherein the link exchange process is a one-time process. 12. The ecosystem security platform of claim 11, wherein said link exchange step comprises authenticating ecosystem members associated with said second VDAX based on genomic eligibility objects of said VDAX. The link module is further configured to yield a second link including a second encoded genomic regulatory instruction, the second link provided to the second VDAX as part of the link exchange process. , the ecosystem security platform of claim 10. 14. The ecosystem security platform of claim 13, wherein said link exchange process is a doubly symmetric process such that said second link is spawned independently of links received from said second VDAX. the link module further determines a second genome control instruction when producing the second link;
Encode the second genome regulatory instruction with the link genome engagement coefficient to obtain a second encoded genome regulatory instruction, wherein the link genome engagement factor is the link mapping sequence and the genome correlated by the root DNA module. determined by the sequence mapping module based on a second modified genomic correlation object modified from the object; and
Generate a genome engagement cargo comprising a link mapping sequence, a second encoded genome regulatory instruction, and encoded link decoding information, wherein the link mapping sequence is not obfuscated in the genome engagement cargo; 2 links contain genomic engagement cargo, and
providing a second link to a second VDAX, the second VDAX based on the link mapping sequence, the encoding link decoding information, and the second genomic data set assigned to the second VDAX; 14. The ecosystem security platform of claim 13, configured to decode two encoded genomic control instructions. 16. The ecosystem security platform of Claim 15, wherein the link mapping sequence remains unencoded in the genome engagement cargo. The binary conversion module further
receiving a second VBLS object from the second VDAX, the second VBLS object including the second encoded digital object and the unencoded metadata; and
decoding said second digital object based on a second genomic engagement factor to obtain a non-encoded second digital object, said second genomic engagement factor comprising a second sequence and said root DNA; determined by the sequence mapping module based on a second modified genomic differentiation object derived by the module from a genomic differentiation object of the genomic data set based on a second genomic regulation instruction;
16. The ecosystem security platform of claim 15, configured to: 2. The ecosystem security platform of Claim 1, wherein the link module is configured to perform a link exchange process across a set of interoperable digital communication media. 2. The ecosystem security platform of Claim 1, wherein the link module is configured to perform a link exchange process across a set of interoperable digital networks. 2. The ecosystem security platform of Claim 1, wherein the link module is configured to perform a link exchange process across a set of interoperable digital devices. 2. The ecosystem security platform of claim 1, wherein the link module is configured to perform a link exchange performed asynchronously with respect to the second VDAX. The ecosystem security of Claim 1, wherein said link module is configured to perform a link exchange process in a symmetrical manner such that said VDAX does not provide a second link to said second VDAX. platform. 2. The ecosystem security platform of claim 1, wherein the linking module is further configured to confirm eligibility correlations for the second VDAX based on genomic eligibility correlation objects of the VDAX. 2. The linking module of claim 1, wherein the linking module is further configured to ascertain link exchange correlations for the second VDAX based on links received from the second VDAX and genomic correlation objects of the VDAX. Ecosystem security platform. 2. The ecosystem security platform of claim 1, wherein the link module is configured to enforce secure exchange of link information using a series of computationally complex functions. 25. The set of computationally complex functions is one of a cryptographic-based function, a cryptographic-free function, or a hybrid function comprising at least one cryptographic-based function and at least one cryptographic-free function. The ecosystem security platform described in. The link module is configured to perform a series of processes to securely exchange link information with the second VDAX to enable asymmetric engagement, wherein the link information exchange is performed with the asymmetric engagement. The ecosystem security platform of claim 1, exhibiting the same level of entropy. 2. The ecosystem security platform of claim 1, wherein the link module comprises a static link module configured to generate and decode static links. 29. The ecosystem security platform of claim 28, wherein the static linking module generates static links according to rules and processes defined by a premier ecosystem VDAX within the digital ecosystem. The linking module includes a dynamic linking module configured to generate and decode a dynamic link, and a link received from the second VDAX, when executed by the VDAX, to the second VDAX. further comprising a set of instructions preferentially applying to the configuration of each of at least one of the root DNA module, the sequence mapping module, or the binary conversion module, such that the modified configuration is executed only upon generation of the provided VBLS. 2. The ecosystem security platform of claim 1, which is a link. 2. The ecosystem security platform of Claim 1, wherein the sequence mapping module is configured to select a published sequence from a respective first portion of each digital object formatted according to a published protocol. The sequence mapping module, in mapping sequences to modified genome differentiation objects,
process the array to derive intermediate values,
2. The eco of claim 1, configured to generate genomic engagement factors using a set of information theory-enhanced computational complexity functions based on the intermediate value and the modified genomic differentiation object. System security platform. The set of information-theoretically facilitated computationally complex functions is either a cryptographic-based function, a cryptographic-free function, or a hybrid function comprising at least one cryptographic-based function and at least one cryptographic-free function. 33. The ecosystem security platform of claim 32, wherein there is. The ecosystem security platform according to claim 1, wherein said genome engagement factor is a binary vector exhibiting a certain entropy. 35. The ecosystem security platform of Claim 34, wherein the specific entropy of the genome engagement factor is greater than or equal to the intrinsic entropy of the sequence. 2. The ecosystem security platform of Claim 1, wherein the sequence mapping module is configured to select a sequence from each first portion of each digital object formatted according to its own protocol. 2. The ecosystem security platform of Claim 1, wherein the root DNA module comprises a CNA module that forms and builds genome-qualifying objects. 38. The ecosystem security platform of claim 37, wherein the CNA modules are configured to employ information theory-facilitated genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems. . 38. The ecosystem security platform of claim 37, wherein said linking module uses said genome eligibility object to confirm genome engagement alignment with said second VDAX. 38. The ecosystem security platform of claim 37, wherein the genomic eligibility object is a CNA object exhibiting a certain entropy, the CNA object enabling genomic processes based on differences and correlations. 41. The ecosystem security platform of claim 40, wherein said CNA object is an N-dimensional binary vector indicative of said particular entropy. 41. The ecosystem security platform of Claim 40, wherein a particular entropy of CNA objects is configurable by a community owner. A CNA module is configured to generate a respective set of CNA objects based on the PNA objects of a VDAX, each set of CNA objects being assigned to a respective set of descendant VDAXes, each CNA object being assigned 41. The ecosystem security platform of claim 40, exhibiting a particular entropy equal to the entropy of the CNA objects of VDAX. The CNA module is configured to use an information theory-enhanced set of computational complexity functions to institute eligibility correlations with respect to other VDAXes based on engagement information provided by CNA objects and other VDAXes. 41. The ecosystem security platform of claim 40, wherein The set of information-theoretically facilitated computationally complex functions is either a cryptographic-based function, a cryptographic-free function, or a hybrid function comprising at least one cryptographic-based function and at least one cryptographic-free function. 45. The ecosystem security platform of claim 44, wherein there is. 41. The ecosystem security platform of claim 40, wherein the CNA module is configured to establish specific relationships between other VDAXes within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's CNA objects. 2. The ecosystem security platform of Claim 1, wherein the root DNA module comprises a PNA module that forms and builds genome-qualified objects. 48. The ecosystem security platform of claim 47, wherein the PNA modules are configured to employ information theory-facilitated genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems. . 48. The ecosystem security platform of claim 47, wherein said linking module uses said genome eligibility object to confirm genome engagement eligibility with said second VDAX. 48. The ecosystem security platform of claim 47, wherein the genomic eligibility object is a PNA object exhibiting a certain entropy, the PNA object enabling genomic processes based on differences and correlations. The PNA object includes a first N-dimensional binary vector and a second N-dimensional binary vector indicative of the particular entropy, wherein the first N-dimensional vector is such that M×T equals N 51. The ecosystem security platform of claim 50, comprising M randomly chosen binary primitive polynomials of degree T, wherein said second N-dimensional vector is determined based on said first N-dimensional binary vector. . 51. The ecosystem security platform of Claim 50, wherein a particular entropy of PNA objects is configurable by a community owner. A PNA module is configured to generate a respective set of PNA objects based on the PNA objects of a VDAX, each set of PNA objects is respectively assigned to a set of respective descendant VDAXs, and each PNA object is 51. The ecosystem security platform of claim 50, exhibiting a certain entropy equal to the entropy of PNA objects in VDAX. The PNA module is configured to use a set of information theory-based computational complexity functions to institute eligibility correlations on other VDAXes based on engagement information provided by PNA objects and other VDAXes. 51. The ecosystem security platform of claim 50. The set of computationally complex functions facilitated by information theory is one of cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. 55. The ecosystem security platform of claim 54, wherein: 51. The ecosystem security platform of Claim 50, wherein the PNA module is configured to establish specific relationships between other VDAXes within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's PNA objects. . 2. The ecosystem security platform of claim 1, wherein said root DNA module comprises an LNA module that forms and builds said genomic correlation object. 58. The ecosystem security platform of claim 57, wherein the LNA modules are configured to employ information theory-facilitated genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems. 58. The ecosystem security platform of Claim 57, wherein said linking module uses said genome eligibility object to verify a link exchange correlation with said second VDAX based on said link. 58. The ecosystem security platform of claim 57, wherein the genome eligibility object is an LNA object exhibiting a certain entropy, the LNA object enabling differential and correlation based genome processing performed during link exchange. 61. The ecosystem security platform of Claim 60, wherein said LNA object is substantially correlated with a second LNA object of said second VDAX. 61. The ecosystem security platform of Claim 60, wherein a particular entropy of LNA objects is configurable by a community owner. 61. The ecosystem security platform of claim 60, wherein said LNA object is an N-dimensional binary vector indicative of said particular entropy. The LNA module uses a set of computational complexity functions based on information theory to generate link eligibility correlations with respect to other VDAXes based on LNA objects and genome engagement cargo provided at each link provided by other VDAXes. 61. The ecosystem security platform of Claim 60, configured to detect a. The set of information-theoretically facilitated computationally complex functions are either cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. 65. The ecosystem security platform of claim 64, wherein there is. 61. The ecosystem security platform of claim 60, wherein the LNA module is configured to establish specific relationships between other VDAXes within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's LNA objects. . The LNA module is configured to generate a respective set of LNA objects based on the LNA objects of the VDAX, each set of LNA objects is respectively assigned to a set of respective descendant VDAXs, each LNA object is 58. The ecosystem security platform of claim 57, exhibiting a particular entropy equal to the entropy of LNA objects of VDAX. The LNA module is configured to modify the genomic correlation object based on a specific set of instructions using a set of information-theoretically-enhanced computational complexity functions, and an information-theoretically-enhanced computational complexity function. 58. The ecosystem of claim 57, wherein the set of functions is one of a cryptographic-based function, a cryptographic-free function, and a hybrid function comprising at least one cryptographic-based function and at least one cryptographic-free function. security platform. The LNA module modifies the genome correlation object based on a set of instructions received from the ancestral VDAX, while modifications to the genome correlation object are used to establish future engagement with respect to the digital ecosystem to which the VDAX belongs. 69. The ecosystem security platform of claim 68, wherein , previously established relationships are unaffected. The LNA module modifies the genome correlation object based on a set of instructions received from the second VDAX in the link to obtain a modified genome correlation object, the modified genome correlation object encoding GRI. 69. The ecosystem security platform of claim 68, as used to determine a link genome engagement factor used to decrypt. 2. The ecosystem security of claim 1, wherein the root DNA module comprises an XNA module that performs genomic processes involving genome correlation objects, including at least one of generating new genome correlation objects and modifying genome correlation objects in VDAX. platform. The XNA module employs information-theoretically-enhanced genomic processes to target the genomic differentiation of the VDAX in the same manner as the second VDAX, according to the genomic regulatory instructions decoded from the links obtained from the second VDAX. 72. The ecosystem security platform of claim 71, configured to modify the . 72. The ecosystem security platform of claim 71, wherein said genome eligibility object is an XNA object exhibiting a certain entropy. 74. The ecosystem security platform of Claim 73, wherein the XNA object is sufficiently correlated with the second XNA object of the second VDAX. 74. The ecosystem security platform of Claim 73, wherein a particular entropy of XNA objects is configurable by a community owner. 74. The ecosystem security platform of Claim 73, wherein said XNA object is an N-dimensional binary vector indicative of said particular entropy. 74. The ecosystem security platform of claim 73, wherein XNA objects are used to establish future differentiation from other VDAXs possessing sufficiently correlated XNA objects. The XNA module is configured to generate a respective set of XNA objects based on the XNA objects of the VDAX, each set of XNA objects is respectively assigned to a set of respective descendant VDAXs, and each XNA object is 74. The ecosystem security platform of claim 73, exhibiting a certain entropy equal to the entropy of XNA objects in VDAX. 74. The ecosystem security platform of Claim 73, wherein the XNA module is configured to modify XNA based on a particular set of instructions using a set of information-theoretically accelerated computational complexity functions. . The XNA module will base XNA on a set of instructions received from its predecessor VDAX such that the updated XNA will be used to establish future differentiation with respect to other VDAXes within the digital ecosystem to which the VDAX belongs. 80. The ecosystem security platform of claim 79, updating. 81. The second VDAX cannot establish future differentiation with the VDAX unless the second VDAX possesses sufficiently correlated and persistently updated XNA. Ecosystem security platform. The set of computationally complex functions facilitated by information theory is one of cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. 80. The ecosystem security platform of claim 79, which is a The ecosystem security platform of claim 1, further comprising an authentication module for prosecuting secure genome-based engagement correlations with respect to the second VDAX according to an information theory-facilitated computational complexity function, wherein the information theory-facilitated The calculated computational complexity functions are any of cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function, the ecosystem security platform. 2. The ecosystem security platform of claim 1, further comprising a master integrity controller including a genomic process controller, an authorization module, and an engagement instance module, the genomic process controller having a master controller genomic dataset assigned thereto. ecosystem security platform. 85. The ecosystem security platform of claim 84, wherein the genomic process controller is configured to engage with one or more platform modules to authenticate and verify integrity of the one or more platform modules. 86. The ecosystem safety platform of claim 85, wherein the genomic process controller verifies integrity and authenticates one or more platform modules based on a master controller genomic dataset and a set of computational complexity functions. 87. The ecosystem security platform of claim 86, wherein the genomic process controller verifies and authenticates the integrity of the one or more platform modules without determining any process or function performed by the one or more platform modules. . The genomic process controller is further configured to verify and authenticate the integrity of any underlying operational processes and functions that connect the one or more platform modules using a set of computationally complex functions. Ecosystem security platform described in 86. 89. The ecosystem safety platform of claim 88, wherein the genomic process controller is further configured to validate, disqualify, or initiate changes to one or more platform modules and underlying operational processes and functions. The authorization module confirms or rejects the operational settings of the another VDAX;
85. The ecosystem security platform of claim 84, wherein the ecosystem security platform is configured to build an ecosystem based on genome controller genomic data and a set of information-theoretically-enhanced computational complexity functions, 91. The ecosystem security platform of claim 90, wherein in response to determining to deny the other VDAX's operational configuration, the authorization module disqualifies or initiates a change in the other VDAX's operational configuration. 85. The engagement instance module of claim 84, wherein the engagement instance module is configured to track security instances in the digital ecosystem of VDAX according to a set of one or more engagement tracking policies defining one or more definitions of security instances. Ecosystem security platform. 92. The engagement instance module is further configured to determine the number of security instances of the digital ecosystem of VDAX according to a set of one or more engagement accounting policies each defining how security instances are counted. Ecosystem Security Platform described in. The Engagement Instances module manages the security of the digital ecosystem according to a set of one or more engagement reporting policies that each define how security instances are reported, to which VDAX security instances are reported, and how often security instances are reported. 94. The ecosystem security platform of Claim 93, further configured to report the number of instances to another VDAX. The ecosystem security platform of Claim 1, wherein VDAX is a member of a digital ecosystem. 96. The ecosystem security platform of claim 95, wherein said digital ecosystem is an enterprise information technology system. 96. The ecosystem security platform of claim 95, wherein the digital ecosystem is a computing device and descendant VDAXs correspond to hardware and digital components of the computing device, respectively. 96. The ecosystem security platform of Claim 95, wherein the digital ecosystem is a traffic grid. 96. The ecosystem security platform of Claim 95, wherein said digital ecosystem is a home network. 96. The ecosystem security platform of Claim 95, wherein the digital ecosystem is a classified computing infrastructure. A system for performing genome security-related controls of a digital ecosystem, comprising:
An Ecosystem VDAX executed by a processing system associated with a Digital Ecosystem Owner, comprising an Ecosystem VDAX consisting of an Ecosystem Instance of the Ecosystem Security Platform, the Ecosystem VDAX:
Maintaining an ancestral genome data set corresponding to a digital ecosystem comprising one or more different digitally generated ancestral genome data objects, each ancestral genome data object exhibiting a particular entropy of a progenitor generating a plurality of respective progeny genomic data sets based on the genomic data set, each progeny genomic data set each derived from one or more digitally generated ancestral genomic data objects, the ancestral genomic data objects from which it was derived containing one or more distinct progeny genome data objects indicating a particular entropy for each of
For each progeny genome dataset,
Assign a progeny genome dataset to each progeny VDAX of multiple progeny VDAXs, such that the progeny VDAX is assigned to the progeny VDAX within the digital community based on each progeny genome dataset assigned to it without further interaction from the ecosystem VDAX. establish a unique non-recurrent engagement with other progeny VDAX of
Control the genomic topology of the digital ecosystem by selectively updating one or more of the progeny genome datasets to influence the ability of specific progeny VDAXs to engage with other VDAXs in the digital ecosystem , that the system is configured as . 102. The system of claim 101, wherein the ancestral genome datasets comprise ancestral genome differentiation objects, each ancestral genome dataset comprising a respective ancestral genome differentiation object. 102, wherein pairs of progeny VDAXs from said plurality of progeny VDAXs are capable of exchanging virtual binary language scripts (VBLS) only if respective progeny genomic differentiation objects of said pairs of progeny VDAXs are sufficiently correlated The system described in . if the first progeny genomic differentiation object of the first progeny VDAX of the set of progeny VDAX is updated and the second progeny genomic differentiation object of the second progeny VDAX of the set of progeny VDAX is not updated, then said progeny VDAX 104. The system of claim 103, wherein the set of is prevented from exchanging VBLS in the future. 104. The system of claim 103, wherein said ancestor genomic differentiation object and said respective progeny genomic differentiation object are XNA objects. The digital ecosystem is
a static ecosystem, the ecosystem platform configured according to a directed architecture, a static ecosystem,
an interactive ecosystem, wherein the ecosystem platform is configured according to a free-form architecture; or
106. The system of claim 105, wherein the ecosystem platform is at least one of a dynamic ecosystem configured according to a dynamic state voluntary architecture. 107. The system of claim 106, further comprising a set of one or more enclave VDAXs, each enclave VDAX corresponding to a respective digital enclave of the digital ecosystem, the enclave VDAX controlling the genomic topology of the respective enclave. A system in which each enclave-specific XNA object is allocated. Each digital enclave contains one or more descendant VDAXs each representing one or more respective cohorts admitted to the digital enclave, and each descendant VDAX contained in each digital enclave is an enclave-specific XNA object of the enclave VDAX 108. The system of claim 107, assigning descendant enclave-specific XNA objects that derive from and are sufficiently correlated with descendent enclave XNA objects of each of other descendant VDAXs contained in the digital enclave. 109. The system of claim 108, wherein each enclave VDAX controls membership to each corresponding digital enclave by assigning enclave-specific XNA objects to cohorts of the digital enclave. 109. The system of claim 108, wherein each enclave VDAX further assigns a respective enclave genome correlation object obtained from the ancestral correlation objects of the ancestral genome data set. 111. The system of claim 110, wherein for each digital enclave, each progeny VDAX of the digital enclave is assigned a respective progeny enclave-specific genomic correlation object derived from the enclave from a respective enclave genomic correlation object of the enclave VDAX of the digital enclave. . 112. The system of claim 111, wherein each progeny VDAX assigns its respective enclave-specific genomic correlation object directly from the enclave VDAX of the digital enclave. 112. The system of claim 111, wherein each progeny VDAX allocates its respective enclave-specific genomic correlation object directly from the ecosystem VDAX. Each progeny VDAX uses its respective progeny enclave-specific genomic correlation object to generate links that establish each unique non-recursive engagement with other progeny VDAXs formed with respect to each digital enclave. 112. The system of paragraph 111. Each link spawned by a descendant VDAX has a unique genomic regulatory instruction that defines how the link hosting the descendant VDAX modifies its enclave-specific XNA objects to produce a non-recurrent VBLS that can only be deciphered by the descendant VDAX. 115. The system of claim 114, providing a Each offspring VDAX uses its respective offspring enclave-specific genomic correlation object to host links provided by other offspring VDAXes in the digital ecosystem, and other offspring VDAXes provide links to offspring VDAXes. 112. The system of claim 111, wherein each digital enclave establishes a unique non-recurring engagement with a progeny VDAX for each digital enclave. Each link hosted by a descendant VDAX is
provide a unique genomic regulatory instruction that defines how the progeny VDAX modifies it,
117. The system of claim 116, wherein enclave-specific genomic differentiation objects generate non-regenerating VBLS that are only readable by other progeny VDAXs that have provided links. 108. The system of claim 107, wherein said enclave VDAX controls said genome network topology by selectively updating progeny enclave-specific genomic data objects of a subset of progeny VDAX participating in said digital enclave. 108. The system of claim 107, wherein the enclave VDAX controls the genomic network topology of the digital enclave without requiring modifications to the physical network topology of the digital enclave. 107. The system of claim 106, wherein the digital ecosystem comprises multiple genome topologies overlaying one or more physical network topologies, wherein the multiple genome topologies exist concurrently and interoperably. 107. The system of Claim 106, wherein the ecosystem VDAX builds and controls a genomic network topology that supports applications with dynamic state attributes. further comprising a set of one or more enclave VDAXs, each enclave VDAX corresponding to a respective digital enclave of the digital ecosystem, and the enclave VDAX being responsible for the ecosystem-designated functions and processes of the digital ecosystem of the genome network topology; 122. The system of claim 121, wherein each enclave controlling portion is assigned a unique genomic data set. One or more further comprising a set of cohort VDAXes participating in the digital ecosystem, wherein the set of cohort VDAXes each control a respective portion of the genomic network topology responsible for specific ecosystem-designated and/or enclave-designated functions and processes. 123. The system of claim 122, comprising cohorts. 124. The system of claim 123, wherein interactions by a set of cohort VDAX are controlled by respective cohort genomic data sets assigned to respective cohort VDAX of the set of cohort VDAX. 125. The system of claim 124, wherein the set of progeny VDAX comprises the set of cohort VDAX. A cohort genomic data set for each cohort of said set of cohort VDAX comprising:
one or more cohort genomic eligibility objects containing one or both of one unique CNA object and one unique PNA object,
one or more cohort genomic correlation objects comprising one or more LNA objects, each LNA object corresponding to each enclave in which the cohort VDAX is a member; and
125. The one or more cohort genomic differentiation objects comprising one or more XNA objects, each XNA object comprising a cohort genomic differentiation object corresponding to a respective enclave in which the cohort VDAX is a member. System as described. The digital ecosystem is a dynamic ecosystem, and the ecosystem security platform is configured according to a voluntary architecture that preserves its operational integrity regardless of how often one or more metric states of the dynamic ecosystem are updated; 122. The system of claim 121, wherein: 128. The system of claim 127, wherein ecosystem VDAX and progeny VDAX collectively control genome network topology in response to particular dynamic metric conditions. 128. The system of claim 127, wherein the system supports multiple genomic network topologies overlaid on a co-existing physical network topology. 128. The system of claim 127, wherein the genome digital network topology is constructed to achieve a controlled level of interoperability. 103. The system of claim 102, wherein said progenitor genomic differentiation object and said respective progeny genomic differentiation object are ZNA objects. A digital ecosystem is a virtual trust execution domain implemented in terms of devices whose offspring VDAX correspond to respective components of the device, where the components of the device combine one or more hardware components and one or more digital components. 132. The system of claim 131, comprising: 132. The system of claim 131, wherein a descendant VDAX from said plurality of descendant VDAXs is configured to perform component binary isolation (CBI) with respect to said virtual trust execution domain. 102. The system of claim 101, further comprising a set of progeny VDAX. Each offspring VDAX consists of a respective set of functionally matched modules, each offspring instance of the ecosystem security platform each configured to perform one or more information theory-facilitated computational complexity functions. 135. The system of claim 134, comprising each descendant instance of an ecosystem security platform as defined. 135, the set of modules of each offspring instance of the ecosystem security platform includes a DNA module configured to manage the genomic datasets of the offspring VDAX and to execute a set of genomic processes based on the genomic datasets; system. 136. The system of claim 135, the set of modules including a link module configured to facilitate secure exchange of links with another VDAX to facilitate inherent bisymmetric engagement. The set of modules includes a sequence mapping module configured to genomically process the sequences to derive a genome engagement factor having a particular entropy, the genome engagement factor transforming the digital object in a non-recurrent manner. 136. The system of claim 135, used for encoding, the sequence being at least one of a public sequence or a private sequence. A set of modules includes a binary conversion module configured to encode digital objects into VBLS objects based on genomic engagement factors determined by the sequence mapping module, each VBLS object being encoded digital objects and 139. The system of claim 138, comprising: , metadata indicative of the published or unpublished sequence used to generate each genomic engagement factor used to encode the encoded digital object. 139. The system of Claim 138, wherein the binary conversion module is further configured to decode received encoded digital objects contained in received VBLS objects based on respective recreated genomic engagement factors. 102. The ecosystem instance of claim 101, wherein the ecosystem instance of the ecosystem security platform has a respective set of modules each configured to perform a respective set of computationally complex information-theoretically facilitated computationally complex functions. System as described. Information Theory Facilitation Based Computation Each set of complex functions performed using cryptographic-based functions, cryptographic-free functions, and at least one stage performed using cryptographic-based functions and cryptographic-free functions 142. The system of claim 141, selected from a hybrid function comprising at least one stage where 143. The system of claim 142, wherein the set of modules of the ecosystem instance includes a root DNA module that manages the ancestral genome dataset and generates the progeny genome dataset. 102. The system of claim 101, wherein said digital ecosystem is a cloud service system. 102. The system of claim 101, wherein said digital ecosystem is an enterprise information technology system. 102. The system of claim 101, wherein the digital ecosystem is a computing device and the descendant VDAXs correspond to hardware and digital components of the computing device, respectively. 102. The system of Claim 101, wherein the digital ecosystem is a traffic grid. 148. The system of claim 147, wherein said traffic grid is an air traffic control grid. 148. The system of claim 147, wherein said traffic grid is an autonomous vehicle traffic grid. 102. The system of Claim 101, wherein the digital ecosystem is a classified computing infrastructure. A method for managing a set of digital entities in a digital ecosystem, comprising:
generating an ancestral genome dataset with a particular entropy by a processing system of an ecosystem VDAX, said ancestral genome dataset being assigned to said ecosystem VDAX;
generating, by a processing system, a plurality of different progeny genome datasets each exhibiting a particular entropy;
for each offspring of a plurality of different offspring genomic data sets, assigning, by the processing system, the offspring genomic data set to a respective digital entity of a set of digital entities, wherein the set of digital entities corresponds to a respective digital entity of each digital entity; enabled to achieve fine control of differences and correlations based on progeny genome datasets. Any pair of digital entities in the set of digital entities confirms the correlation of the respective genomic datasets of the pair of digital entities, and based on the confirmed correlation of the progeny genomic datasets of the pair of digital entities, the respective progeny 152. The method of claim 151, configured to distinguish the genomic dataset from any other progeny genomic dataset to form a unique non-reproducible relationship within the digital community. 153. The method of claim 152, wherein said pair of digital entities are each configured to independently ascertain correlations using a specific set of information theory-enhanced computational complexity functions. The set of computationally complex functions facilitated by information theory is one of cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. 154. The method of claim 153, wherein 4., wherein each digital entity of the set of digital entities is configured to independently distinguish between respective progeny genomic data sets using a second set of information-theoretically facilitated computational complexity functions. The method described in 153. The second set of computationally complex functions is one of cryptographic-based functions, cryptographic-free functions, and hybrid functions comprising at least one cryptographic-based function and at least one cryptographic-free function. 156. The method of claim 155. 153. The method of claim 152, wherein in response to forming the unique non-recurring relationship, the pair of entities generate a unique, decipherable only by the pair of entities based on differentiated progeny genomic data. The method of engaging by generating and exchanging non-recurring Virtual Binary Language Scripts (VBLS), wherein a pair of entities are 158. The method of claim 157, wherein the VBLS is composed of encoded digital objects carrying information-theoretically driven genomic attributes of respective genomic datasets of a set of digital entities. 152. The method of claim 151, wherein the specified entropy is a configurable level of entropy defined by community owners associated with ecosystem VDAX. 152. The method of claim 151, wherein digital entities collectively enable virtual authentication, virtual affiliation, and virtual agility. 152. The method of claim 151, wherein each progeny genomic data set comprises a genomic correlation object exhibiting a particular entropy and a genomic differentiation object exhibiting a particular entropy. 163. The method of claim 162, wherein each progeny genome data set comprises a respective genome qualification object exhibiting a particular entropy. 152. The claim of claim 151, wherein the digital ecosystem comprises one or more digital enclaves formed based on respective mutual identities of interest represented by controlled differences and correlations in progeny genomic datasets. Method. 164. The method of claim 163, wherein each digital enclave comprises one or more cohorts that share respective mutual benefits represented by controlled differences and correlations in progeny genomic datasets.

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