CN114020841A - Data sharing system, method, storage medium and equipment - Google Patents
Data sharing system, method, storage medium and equipment Download PDFInfo
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- CN114020841A CN114020841A CN202111296300.5A CN202111296300A CN114020841A CN 114020841 A CN114020841 A CN 114020841A CN 202111296300 A CN202111296300 A CN 202111296300A CN 114020841 A CN114020841 A CN 114020841A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
Abstract
The invention provides a data sharing system, a data sharing method, a storage medium and a device, belonging to the technical field of data protection; the data of each node is combined with a federal learning multi-party safety calculation privacy safety framework at a side chain, and a shared data model is built based on the identity mechanism verification of the side chain; in the primary side chain data sharing privacy protection, firstly, the data nodes on the side chain are subjected to identity verification, and the safety of the data nodes on the side chain is ensured. Then, in data sharing, the data nodes can share data, model data and keep private according to the importance of the data, so that the safety of the data can be guaranteed, the data is not abused and tampered, and the model data sharing in some public service fields and financial fields can be guaranteed, so that the data can better complete the closed loop of data sharing. Due to the adoption of the data model sharing of the side chain, the data utilization rate and the data privacy protection security can be greatly improved.
Description
Technical Field
The invention belongs to the technical field of data protection, and particularly relates to a data sharing system, a data sharing method, a storage medium and data sharing equipment.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art or the prior art.
Blockchains are a term of art in information technology. Essentially, the system is a shared database, and hash values or information stored in the database has the characteristics of 'unforgeability', 'whole-course trace', 'traceability', 'public transparency', 'collective maintenance', and the like. The method is a novel use of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like, a side chain is established on a block chain, and data consensus and authentication of a union are established. By combining the privacy calculation technology of federal learning, the federal learning multiparty safety calculation is a machine learning framework, and can effectively help a plurality of organizations to perform data use and machine learning modeling under the condition of meeting the requirements of user privacy protection, data safety and government regulations. The application technology can be used in the fields of financial institutions, public services, data authentication and other wide application life scenes.
However, in practical applications, after the side chain technology of the blockchain is authenticated in the federation, the nodes of the side chain can share data on the link, so that data of some important nodes is easily tampered, and privacy protection of data of the blockchain is limited, so that the data on the link can be used, and privacy protection cannot be performed on the important data of the nodes to establish a model.
In an actual side chain scene, because a side chain and a federal learning privacy protection technology are separated from each other, good efficiency cannot be exerted in respective fields, and data required to be applied by a node cannot be effectively combined, the following aspects are mainly provided:
1) all members on the block chain can enter the side chain after being verified, and the whole member authentication is carried out on the alliance chain to form a side chain;
2) on the side chain, each node can share the data of each node, and under a certain mechanism, the data of the node can be modified;
3) for the nodes on the side chain, when other data needs to be used among the nodes, the data is shared in a block chain account book mode, but in some special scenes, due to the importance of the data, the data is not required to be shared, and the data is only required to be applied to another node;
4) compared with the federal learning privacy technology, due to the fact that complete customization is not available, all data of the federation learning privacy technology are processed to obtain a data model, and ownership of the data can be acquired in a customized mode in a special scene.
Disclosure of Invention
To overcome the disadvantages of the prior art, it is an object of the present invention to provide a data sharing system, method, storage medium and device based on block chain privacy encryption, which can solve the above problems.
The design principle is as follows: the data sharing technology based on the block chain side chain security privacy protection homomorphic encryption technology is provided, homomorphic encryption and differential privacy technologies in cryptography are utilized, and the data sharing technology comprises alliance nodes, privacy computation, data nodes, multiparty security computation scheduling, homomorphic encryption technology, secret sharing and OT protocols.
Protocol-related terms illustrate:
MPC-Secure Multi-Party computing, a Secure Multi-Party computing algorithm that protects data security and privacy.
OT-overhead Transfer, which is an inadvertent Transfer, is a secure selection, Transfer protocol.
The overall scheme is as follows: in order to solve the above problem, the overall design of the present application is as follows.
A data sharing system based on blockchain privacy encryption, the data sharing system comprising:
the data node data storage module is used for storing the data of all the coalition members;
the data verification module comprises an authorization unit and a verification unit, wherein the verification unit authorizes the side chain in the alliance and judges whether the data can be shared and modeled, and the verification unit authenticates the identity and data assets of the data in the side chain;
the hash data protection module carries out privacy encryption processing, hash data modeling and sharing on the node data which completes identity authentication and authority verification through multi-party security calculation;
and the user terminal module is used for uploading local node data, inquiring, applying and displaying shared data.
Furthermore, the block chain side chain corresponding to each coalition member comprises a data node, an authorization node, a verification node and a data model node, and each node of the current side chain completes closed-loop control of data sharing and protection through multi-party safety calculation.
Furthermore, a safety mechanism is established by the side chain node of each block chain of the alliance according to the safety level system of data of the side chain node, and privacy safety is established in a mode of a federal model and multi-party calculation.
Furthermore, the data model node controls each data sharing and must perform data storage, data acquisition, identity verification, security calculation algorithm and authority control so as to complete data submission and verification of the four nodes.
Further, the data node stores data for the corresponding side chain based on the data node data storage module.
Further, the verification node and the authorization node are based on a data verification module and are used for identity authentication and data asset authentication in the side chain, and whether authorization can be carried out on data sharing and data modeling.
Furthermore, the verification node binds the identity and the data assets in a serial code mode, and judges the uniqueness of the data assets through the one-to-many idempotent relation, so that the same junk data assets are prevented from being generated.
The invention also discloses a data sharing method based on block chain privacy encryption, which comprises the following steps:
establishing a side chain node alliance, using each data center and/or user as an alliance member to uplink own data, establishing a safety mechanism according to a safety level system of the own data, and completing the construction of the side chain node alliance through multi-party safety calculation;
performing multi-party safety calculation scheduling, and performing identity verification on the data nodes of each side chain to ensure that a data sharing mode on the data nodes on the side chains conforms to a safety mechanism;
and the target side chain pushes three access results, namely privacy encryption processing, Hash data modeling or sharing, to the user according to the security level of the data of the target side chain.
Compared with the prior art, the invention has the beneficial effects that: the method combines the data of each node with a federal learning multi-party safety calculation privacy safety framework at a side chain, verifies the identity mechanism based on the side chain, and builds a shared data model; the node data on the side chain can share data on the side chain, keep the consistency of the data, calculate privacy and safety according to federal learning and multi-party safety, ensure the data safety and privacy of important nodes, avoid being stolen, perform data modeling on the data of the important nodes according to the service application scenes of other nodes, and enable other nodes to acquire related service scene data under the condition of not owning data ownership. In practical application scenarios, the following scenario values are mainly available:
1) the federation nodes can share aiming at personal privacy data through modeling;
2) the individual privacy data can be selectively shared on the alliance nodes through data modeling;
3) for the whole alliance node, a data model can be perfected and private data of the node can be guaranteed;
4) the data utilization and data modeling are facilitated, and the privacy between the data utilization and the data modeling is guaranteed;
due to the adoption of the data model sharing of the side chain, the data utilization rate can be improved by 50 percent; the security of data privacy protection can be improved by 60%.
Drawings
FIG. 1 is a schematic diagram of a data sharing system based on block chain privacy encryption according to the present invention;
FIG. 2 is a system technology architecture diagram;
FIG. 3 is an application scenario diagram;
FIG. 4 is a schematic diagram of a method.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be understood that "system", "module", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
First embodiment
Referring to fig. 1, the data sharing system includes a data node data storage module, a data verification module, a hash data protection module, and a user terminal module.
And the data node data storage module is used for storing the data of all the coalition members.
And the data verification module comprises an authorization unit and a verification unit, the verification unit authorizes the side chain in the alliance to determine whether the data can be shared and modeled, and the verification unit authenticates the identity and the data assets of the data in the side chain.
And the Hash data protection module performs privacy encryption processing, Hash data modeling and sharing on the node data which completes identity authentication and authority verification through multi-party security calculation.
And the user terminal module is used for uploading local node data, inquiring, applying and displaying shared data. The user terminal includes but is not limited to a mobile phone, a computer, a tablet and the like.
Referring to fig. 2, in the system technical architecture diagram, the system utilizes homomorphic encryption and differential privacy techniques in cryptography, and specifically, the blockchain side chain corresponding to each coalition member includes: the system comprises a data node, an authorization node, a verification node and a data model node; multi-party secure computation and the techniques used in the computation (privacy technique, homomorphic encryption, secret sharing, OT protocol technique, etc.), but the present technique mainly teaches how the secure privacy computation technique accomplishes the closed loop of data sharing and protection in the side chain of the block chain.
Furthermore, a safety mechanism is established by the side chain node of each block chain of the alliance according to the safety level system of data of the side chain node, and privacy safety is established in a mode of a federal model and multi-party calculation.
Furthermore, the data model node controls each data sharing and must perform data storage, data acquisition, identity verification, security calculation algorithm and authority control so as to complete data submission and verification of the four nodes. The data model nodes are mainly built for shared data models, data modeling is carried out on data unified data shared by all the nodes at the nodes, and all the nodes can be used and built through authorized nodes; the node also needs to verify the data on the data model through a verification node to ensure the uniqueness of the node entering the data model.
Further, the data node stores data for the corresponding side chain based on the data node data storage module. The data node is similar to that in everyday applications: financial institutions, public service institutions, and telecommunications regulatory agencies.
Further, the verification node and the authorization node are based on a data verification module and are used for identity authentication and data asset authentication in the side chain, and whether authorization can be carried out on data sharing and data modeling.
The verification node judges the uniqueness of the data assets through the one-to-many power relation by binding the identity and the data assets in a serial code mode, the same data assets cannot be generated, and the same junk data assets are prevented from being generated.
The authorization node is whether the side chain authorizes in the alliance to share and model the data, and the authorized data node user can share and model the shared data on the side chain. If the data does not pass through the authorization node, the data asset sharing and the data modeling cannot be carried out, and particularly in the case of privacy data protection, the emphasis on authorization is placed more.
The block chain side chain safety privacy calculation technology is mainly used for synchronously encrypting data of a side chain node of a block chain after a side chain forms a alliance node, selectively performing data sharing on the block chain side chain, completing data modeling sharing in a homomorphic encryption mode, and preventing other nodes on the side chain from tampering and stealing the data in a privacy encryption mode.
Application example: referring to fig. 3, the technical field can be used for forming a side chain for different banking financial institutions, each bank belongs to a node of the side chain, and each banking financial institution can determine whether a client group has loan data in other nodes, whether a bad quantity exists, and whether a blacklist exists before lending, so as to ensure the safety of own business. The method can also be used in the field of government affair public service, and proposes a summarized user data model on the nodes of the side chain aiming at the user model data established by different departments, and the data is perfected aiming at the user data model by different department nodes, and each department node can be used aiming at the user data model, thereby achieving the application of business scenes.
The side chains are mainly used for building nodes of different data departments, in the process of jointly modeling and scoring service scene data sharing or encryption before credit loan for one time, a financial institution is required to participate in and be added into the same side chain to form each data node on the side chain, then a data storage mechanism is built on the nodes of the side chains, and a data model is built according to privacy safety calculation rules. In the data sharing and privacy encryption processes, the two modes are completely different, and for the sharing mechanism, a data model can be established according to each side chain node, and secret sharing is established on the data model, so that data sharing is guaranteed.
For the data encryption mechanism model, each side chain node establishes a security mechanism according to the data security level system of the side chain node, and establishes privacy security through a federal model and a multi-party calculation mode, so that the side chain nodes only know the result of the whole model data, but not the source tracing and information of the data, and the privacy security of the data is ensured.
For example, in the technical architecture diagram, the side chain is composed of a data node, a verification node, an authorization node and a data model node, and the multi-party security computation module is used as a technical capability module ("multi-party security computation scheduling" in the technical architecture diagram) and is convenient to call in a specific service scene, so that the user-defined shared private data sharing is achieved. The data sharing and encryption only relate to side chain business logic, and a transaction scene does not need to be carried out on a main chain, namely the technology does not relate to the main chain. The data model node of the side chain controls that each data sharing needs to carry out submission and verification (data storage, data acquisition, identity verification, security calculation algorithm and authority control) of four nodes. Namely, the data node firstly needs to complete identity authentication and authority control in a side chain, and data can be shared or encrypted after the data node is confirmed each time. In any node, if data needs to be shared or other node data needs to be shared, the data model for data acquisition can be established only through identity authentication and authority control of other nodes. The hash value corresponding to the data model is recorded in the block module to form a data hash value, the hash value of one block is formed and serialized into a hash value stream, and the data is shared to other data nodes through the authorized nodes of the side chain; in a data node, if the data node needs to protect the data of the data node from being damaged, that is, the data node is not modified and the data model is not changed by the data node of the side chain, the data node needs to execute an MPC protocol on the data of the data node through external multi-party secure computation to perform encrypted computation on the data of the data node, and the computation process is based on the implementation of homomorphic encryption + secret sharing + OT, so that the data of the data node forms federation consensus on the side chain, the data of the data node is encrypted, and the data model node can be completed under the condition of data sharing, so that a user can know the application scene result of the used data, and the process of data establishment is not clear. For example, bank a organization wants to know the minuscule information, which is collected by bank B. At the moment, the bank organization A can learn about 28 years old, sex men, 3 years of work, doctor's study and hobby sports through the data model node; however, the bank A only learns the information through the data model nodes, but how the information is acquired, the bank A organization is completely unclear, and the information may be a data model established by other data nodes of the side chain or a data model node established by other bank organizations; in another case, the data in the data node is completely sealed, is not shared outside, and can only be used by itself, the data node needs to perform private data protection through external multiparty secure computation, encrypts the data by a password, and cannot decrypt the data if other data nodes cannot know a decryption key, so that the data can only be used by itself, and after the data is obtained by a side chain alliance from the outside, the data is also an encrypted hash value, and the value cannot be used.
Second embodiment
A data sharing method based on block chain privacy encryption, referring to fig. 4, the method includes:
establishing a side chain node alliance, using each data center and/or user as an alliance member to uplink own data, establishing a safety mechanism according to a safety level system of the own data, and completing the construction of the side chain node alliance through multi-party safety calculation;
performing multi-party safety calculation scheduling, and performing identity verification on the data nodes of each side chain to ensure that a data sharing mode on the data nodes on the side chains conforms to a safety mechanism;
and the target side chain pushes three access results, namely privacy encryption processing, Hash data modeling or sharing, to the user according to the security level of the data of the target side chain.
In the process of secure shared privacy encryption protection of a plurality of sides of a side-chain data node, each data node has three modes for data, wherein the three modes are as follows: sharing, data modeling, and privacy.
In the sharing process, after the data nodes store the data, the data can be verified, after the uniqueness of the data is verified, the verified data is subjected to Hash data protection, in the data sharing capacity, uniform business normalization needs to be carried out on the data through data rules, when each data node shares the data, the data needs to be cleaned according to different side chain authorities, and the data is applied to the node of the data node.
In data modeling, a data node encapsulates stored data and transmits the data to a multi-party security computing framework (capable of viewing an application scene graph) through a transmission layer (responsible for communication between two processes in a host), and the multi-party security computing framework sends computing invitations like nodes in a side chain and notifies the nodes to execute data privacy encryption operation by adopting different algorithm strategies according to the number of the side chain nodes. In the whole calculation protocol, the calculation logic is externally disclosed, so that other people cannot acquire original data of the calculation protocol, and cannot perform operation modification on the data, so that the rights and interests of data nodes and the value of the data are protected. The calculation participants only need to participate in the calculation protocol, data calculation can be completed without depending on a third party, and the participants can not deduce the original data when taking the calculation result, so that the safety of the data is ensured. Secondly, when data is obtained by the multi-party safety computing framework, the nodes of the side chains initiate a computation task scheduling of the MPC, and after authorization and permission confirmation of the side chains are passed, original data, IP addresses of local network cards, IP of servers and the like are sent to each node through a network layer, so that the data model nodes know which data nodes the data comes from, and cooperative computing operation is performed conveniently. And searching data nodes of other data holders conforming to similar data types in the side chain, and performing safe system calculation. And the data nodes participating in the system calculation and holding the data acquire the data according to the calculation logic and the data nodes, and perform the system calculation on the data by using the MPC calculation task together. On the premise of guaranteeing the privacy of the data and the user rights, all parties obtain correct data feedback, and the node data is not disclosed to any data node participating party in the whole process. According to the difference of the number of the nodes, the method can be divided into a 2PC with only two participants and a universal MPC with a plurality of participants (more than or equal to 3). In order to ensure the expansibility of a subsequent system, at least 2 system participants adopt MPC algorithm for processing. And allowing a plurality of data owners to perform collaborative calculation under the condition that the data sources of the data owners are not clear of each other, outputting a final result and finishing modeling of the data.
In privacy protection, when a data node does not want to share data on a side chain or model data, privacy encryption calculation is performed on the data of the data node by executing privacy calculation capacity, the data is encrypted to ensure the safety and uniqueness of the data, and after the data is encrypted, privacy encryption processing is completed to form a hash block. In the data chaining process, the data nodes firstly control non-uplink on the data nodes through the authority nodes, after privacy calculation protection is called, data are encrypted, a new data module is formed after encryption, encryption results are synchronously transmitted to the data nodes according to the data rules of the side chains, a unified book is formed, however, due to the fact that the data are encrypted, even if the data are obtained at other data nodes, under the condition that decryption keys are not obtained, application scenes of the data cannot be used, and therefore privacy data are protected.
In summary, in the data sharing privacy protection of the side chain, the data node on the side chain needs to be authenticated first to ensure the data node on the side chain. Then, in data sharing, the data node can share data in three ways according to data importance, and the three ways are: sharing, data modeling and privacy. Therefore, the data security can be ensured, the data is not abused and tampered, and shared data modeling in some public service fields and financial fields can be ensured, so that the data can better complete a data sharing closed loop.
Third embodiment
A computer storage medium having computer instructions stored thereon, characterized in that: the computer instructions when executed perform the foregoing method. For details, the method is described in the foregoing section, and is not repeated here.
It will be appreciated by those of ordinary skill in the art that all or a portion of the steps of the various methods of the embodiments described above may be performed by associated hardware as instructed by a program that may be stored on a computer readable storage medium, which may include non-transitory and non-transitory, removable and non-removable media, to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visualbasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Fourth embodiment
The invention also provides a device comprising a memory and a processor, the memory having stored thereon computer instructions capable of being executed on the processor, the processor executing the computer instructions to perform the aforementioned method. For details, the method is described in the foregoing section, and is not repeated here.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A data sharing system based on blockchain privacy encryption, the data sharing system comprising:
the data node data storage module is used for storing the data of all the coalition members;
the data verification module comprises an authorization unit and a verification unit, wherein the verification unit authorizes the side chain in the alliance and judges whether the data can be shared and modeled, and the verification unit authenticates the identity and data assets of the data in the side chain;
the hash data protection module carries out privacy encryption processing, hash data modeling and sharing on the node data which completes identity authentication and authority verification through multi-party security calculation;
and the user terminal module is used for uploading local node data, inquiring, applying and displaying shared data.
2. The data sharing system according to claim 1, wherein: the block chain side chain corresponding to each alliance member comprises a data node, an authorization node, a verification node and a data model node, and each node of the current side chain completes closed-loop control of data sharing and protection through multi-party safety calculation.
3. The data sharing system of claim 2, wherein: and a safety mechanism is established by the side chain node of each alliance block chain according to the safety level system of the data of the side chain node, and privacy safety is established by a federal model and a multi-party calculation mode.
4. The data sharing system of claim 2, wherein: the data model node controls each data sharing and must perform data storage, data acquisition, identity authentication, security calculation algorithm and authority control so as to complete data submission and authentication of the four nodes.
5. The data sharing system of claim 2, wherein: and the data nodes store data for the corresponding side chains based on the data node data storage module.
6. The data sharing system of claim 2, wherein: the verification node and the authorization node are based on a data verification module and are used for identity authentication and data asset authentication in the side chain, and whether authorization can be carried out on data sharing and data modeling.
7. The data sharing system according to claim 6, wherein: the verification node judges the uniqueness of the data assets through the one-to-many idempotent relation by binding the identity and the data assets in a serial code mode, and prevents the same junk data assets from being generated.
8. A data sharing method based on block chain privacy encryption is characterized by comprising the following steps:
establishing a side chain node alliance, using each data center and/or user as an alliance member to uplink own data, establishing a safety mechanism according to a safety level system of the own data, and completing the construction of the side chain node alliance through multi-party safety calculation;
performing multi-party safety calculation scheduling, and performing identity verification on the data nodes of each side chain to ensure that a data sharing mode on the data nodes on the side chains conforms to a safety mechanism;
and the target side chain pushes three access results, namely privacy encryption processing, Hash data modeling or sharing, to the user according to the security level of the data of the target side chain.
9. A computer storage medium having computer instructions stored thereon, characterized in that: the computer instructions when executed perform the method of claim 8.
10. An apparatus comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, the apparatus comprising: the processor, when executing the computer instructions, performs the method of claim 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115021962A (en) * | 2022-04-28 | 2022-09-06 | 北京八分量信息科技有限公司 | Distributed trusted privacy computing system |
| CN116049322A (en) * | 2023-04-03 | 2023-05-02 | 安羚科技(杭州)有限公司 | Data sharing platform and method based on privacy calculation |
| CN116595574A (en) * | 2023-04-14 | 2023-08-15 | 京信数据科技有限公司 | Device and method for safely publishing and quoting privacy computing model |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115021962A (en) * | 2022-04-28 | 2022-09-06 | 北京八分量信息科技有限公司 | Distributed trusted privacy computing system |
| CN115021962B (en) * | 2022-04-28 | 2023-08-22 | 北京八分量信息科技有限公司 | Distributed trusted privacy computing system |
| CN116049322A (en) * | 2023-04-03 | 2023-05-02 | 安羚科技(杭州)有限公司 | Data sharing platform and method based on privacy calculation |
| CN116049322B (en) * | 2023-04-03 | 2023-06-13 | 安羚科技(杭州)有限公司 | Data sharing platform and method based on privacy calculation |
| CN116595574A (en) * | 2023-04-14 | 2023-08-15 | 京信数据科技有限公司 | Device and method for safely publishing and quoting privacy computing model |
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