CN112491862A - Distributed encryption method and device - Google Patents

Distributed encryption method and device Download PDF

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CN112491862A
CN112491862A CN202011319982.2A CN202011319982A CN112491862A CN 112491862 A CN112491862 A CN 112491862A CN 202011319982 A CN202011319982 A CN 202011319982A CN 112491862 A CN112491862 A CN 112491862A
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iteration
distributed encryption
encryption scheme
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CN112491862B (en
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王智明
徐雷
陶冶
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China United Network Communications Group Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
    • H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
    • H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
    • H04L9/0852—Quantum cryptography
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
    • H04L9/0861—Generation of secret information including derivation or calculation of cryptographic keys or passwords
    • H04L9/0869—Generation of secret information including derivation or calculation of cryptographic keys or passwords involving random numbers or seeds

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Abstract

The invention provides a distributed encryption method and a distributed encryption device, belongs to the technical field of encryption, and can at least partially solve the problems of high delay, high computational power consumption and low encryption efficiency of the existing encryption method. The distributed encryption method of the embodiment of the invention comprises the following steps: acquiring a plurality of distributed encryption requests of edge equipment, wherein each distributed encryption request comprises an evaluation index; carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme; and sending the distributed encryption scheme to edge equipment so that the edge equipment can encrypt according to the distributed encryption scheme.

Description

Distributed encryption method and device
Technical Field
The invention belongs to the technical field of encryption, and particularly relates to a distributed encryption method and device.
Background
With the rapid development of technologies, especially block chain technologies, the conventional encryption adopted at present is gradually unable to adapt to the network attacks which are increasingly upgraded and varied, and the problems of high delay, high computational power consumption, low encryption efficiency and the like in the encryption process are increasingly prominent.
Disclosure of Invention
The invention at least partially solves the problems of high delay, high computational power consumption and low encryption efficiency of the existing encryption method, and provides a distributed encryption method with low delay, low computational power consumption and high encryption efficiency.
One aspect of the present invention provides a distributed encryption method, the method comprising:
acquiring a plurality of distributed encryption requests of edge equipment, wherein each distributed encryption request comprises an evaluation index;
carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme;
and sending the distributed encryption scheme to edge equipment so that the edge equipment can encrypt according to the distributed encryption scheme.
Optionally, the performing deep analysis and deep analysis evaluation on the evaluation index in the multiple distributed encryption requests to generate a distributed encryption scheme includes:
starting a new iteration loop, resetting the iteration times, setting the maximum iteration times and setting the iteration parameters of initial iteration according to evaluation indexes in the distributed encryption requests;
analyzing iterative parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy, and generating a distributed encryption scheme and iterative parameters of next iteration;
judging whether the iteration times reach a threshold value, if so, ending the circulation, and outputting a distributed encryption scheme obtained by the iteration as a distributed encryption scheme sent to the edge equipment;
if not, evaluating the distributed encryption scheme obtained by the iteration according to an evaluation function, and under the condition that the distributed encryption scheme obtained by the iteration does not meet the evaluation function, adding 1 to the iteration number, returning to the step of analyzing the iteration parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy, and generating the distributed encryption scheme and the iteration parameters of the next iteration;
and under the condition that the distributed encryption scheme obtained by the iteration meets the evaluation function, ending the loop, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
Further optionally, in the k-th iteration, the iteration parameter includes encryption efficiency
Figure BDA0002792574560000021
Rate of delay
Figure BDA0002792574560000022
Computing power consumption
Figure BDA0002792574560000023
Where i is 1,2, … m, j is 1,2, … n, t is 1,2, …, p, m is the maximum value of all values of i, n is the maximum value of all values of j, and p is the maximum value of all values of t.
Further optionally, the evaluation function at the k-th iteration is:
Figure BDA0002792574560000024
where P represents a probability.
Further optionally, analyzing iterative parameters by using a multilayer convolution neuron, quantum key distribution and a deep unsupervised learning strategy, and generating a distributed encryption scheme, including generating the distributed encryption scheme by using an optimization function;
the optimization function at the kth iteration is:
Figure BDA0002792574560000025
Skey＝H{Random[q Mod w]},q∈[1,2,…,+∞],w≤Ω，
Rkey＝H{Random[ρMod w]},ρ∈[1,2,…,+∞],w≤Ω，
Figure BDA0002792574560000026
Figure BDA0002792574560000027
wherein H is a hash function, Random is a function for generating Random numbers, text is data to be encrypted,
Figure BDA0002792574560000031
representing an exclusive or operation.
Further optionally, analyzing iteration parameters by using a multilayer convolution neuron, quantum key distribution and a deep unsupervised learning strategy to generate iteration parameters of next iteration, wherein the generation of the iteration parameters of the next iteration by using a supervision function is included;
the supervision function at the kth iteration is:
Figure BDA0002792574560000032
Figure BDA0002792574560000033
Figure BDA0002792574560000034
wherein the content of the first and second substances,
Figure BDA0002792574560000035
AGmaxfor maximum encryption efficiency, EGminIs the minimum delay rate, CGminFor minimum computational power consumption, mod is the remainder operation.
Another aspect of the present invention provides a distributed encryption apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of distributed encryption requests of edge equipment, and each distributed encryption request comprises an evaluation index;
the analysis module is used for carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme;
and the sending module is used for sending the distributed encryption scheme to the edge equipment so that the edge equipment can encrypt the distributed encryption scheme.
Optionally, the analysis module includes:
the initial unit is used for resetting the iteration times when a new iteration cycle starts, setting the maximum iteration times and setting the iteration parameters of initial iteration according to the evaluation indexes in the distributed encryption requests;
the analysis unit is used for analyzing the iteration parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy and generating a distributed encryption scheme and iteration parameters of next iteration;
the first judgment unit is used for judging whether the iteration times reach a threshold value, if so, the circulation is ended, and the distributed encryption scheme obtained by the iteration is output as the distributed encryption scheme sent to the edge equipment;
the second judgment unit is used for evaluating the distributed encryption scheme obtained by the iteration according to the evaluation function when the first judgment unit judges that the iteration frequency does not reach the threshold value, and adding 1 to the iteration frequency under the condition that the distributed encryption scheme obtained by the iteration does not meet the evaluation function;
and under the condition that the distributed encryption scheme obtained by the iteration meets the evaluation function, ending the loop, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
Further optionally, in the k-th iteration, the iteration parameter includes encryption efficiency
Figure BDA0002792574560000041
Rate of delay
Figure BDA0002792574560000042
Computing power consumption
Figure BDA0002792574560000043
Where i is 1,2, … m, j is 1,2, … n, t is 1,2, …, p, m is the maximum value of all values of i, n is the maximum value of all values of j, and p is the maximum value of all values of t.
Further optionally, the evaluation function at the k-th iteration is:
Figure BDA0002792574560000044
where P represents a probability.
In the distributed encryption method and device provided by the embodiment of the invention, the optimal distributed encryption scheme is obtained through analyzing the distributed encryption request, and the edge device performs distributed encryption according to the obtained distributed encryption scheme, so that distributed encryption with low delay, low computational power consumption and high encryption efficiency is realized.
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Fig. 1 is a schematic flow chart of a distributed encryption method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a partial flow of a distributed encryption method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a distributed encryption apparatus according to an embodiment of the present invention;
fig. 4 is a block diagram schematically illustrating an analysis module of a distributed encryption apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
It is to be understood that the specific embodiments and figures described herein are merely illustrative of the invention and are not limiting of the invention.
It is to be understood that the embodiments and features of the embodiments can be combined with each other without conflict.
It is to be understood that, for the convenience of description, only parts related to the present invention are shown in the drawings of the present invention, and parts not related to the present invention are not shown in the drawings.
It should be understood that each unit and module related in the embodiments of the present invention may correspond to only one physical structure, may also be composed of multiple physical structures, or multiple units and modules may also be integrated into one physical structure.
It will be understood that, without conflict, the functions, steps, etc. noted in the flowchart and block diagrams of the present invention may occur in an order different from that noted in the figures.
It is to be understood that the flowchart and block diagrams of the present invention illustrate the architecture, functionality, and operation of possible implementations of systems, apparatus, devices and methods according to various embodiments of the present invention. Each block in the flowchart or block diagrams may represent a unit, module, segment, code, which comprises executable instructions for implementing the specified function(s). Furthermore, each block or combination of blocks in the block diagrams and flowchart illustrations can be implemented by a hardware-based system that performs the specified functions or by a combination of hardware and computer instructions.
It is to be understood that the units and modules involved in the embodiments of the present invention may be implemented by software, and may also be implemented by hardware, for example, the units and modules may be located in a processor.
The distributed encryption method of the present embodiment is used for a blockchain.
The block chain is a distributed shared account book and a database, and has the characteristics of decentralization, no tampering, trace retaining in the whole process, traceability, collective maintenance, openness and transparency and the like. The characteristics ensure the honesty and the transparency of the block chain and lay a foundation for creating trust for the block chain. And the rich application scenes of the block chains basically solve the problem of information asymmetry based on the block chains, and realize the cooperative trust and consistent action among a plurality of main bodies. The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. The Blockchain (Blockchain) is an important concept of bitcoin, is essentially a decentralized database, and is used as a bottom-layer technology of bitcoin, and is a series of data blocks which are generated by correlation by using a cryptographic method, wherein each data block contains information of a batch of bitcoin network transactions for verifying the validity of the information and generating a next block, so that the Blockchain-based distributed encryption method has important significance.
The distributed encryption method of the embodiment is mainly used for a distributed encryption scene of a terminal, and specifically, the scene mainly comprises three parts: the edge access layer includes an edge device (e.g., a distributed cellular encryption edge device), which may be specifically a terminal (e.g., a mobile phone, a computer, etc.), and implements distributed encryption, generation and transmission of a distributed encryption request, and reception of a distributed encryption scheme. The access layer, including the gateway, enables access to the operator network and data (specifically, a distributed encryption request, a distributed encryption scheme) transmission. And the core layer comprises a core server and is used for analyzing the distributed encryption request and generating a distributed encryption scheme.
The edge device may generate an evaluation index according to a desired distributed encryption index, encryption efficiency (data amount successfully encrypted in unit time/total data amount to be encrypted in unit time), delay rate (data amount not encrypted in unit time/total data amount to be encrypted in unit time), power consumption (power consumed in unit time), and the like, and may further generate a distributed encryption request.
In the distributed encryption scenario of the edge device, the processing flow of the distributed encryption method may be: the edge device generates a distributed encryption request and sends the distributed encryption request to the core server through the gateway, the core server analyzes the distributed encryption request to generate a distributed encryption scheme, and the distributed encryption scheme is sent to the edge device through the gateway.
In the application scenario, the core server is used for analyzing the distributed encryption request to obtain an optimal distributed encryption scheme, and the edge device performs distributed encryption according to the obtained distributed encryption scheme to realize distributed encryption with low delay, low computational power consumption and high encryption efficiency.
The following describes in detail the functions of distributed encryption (i.e., analyzing a plurality of distributed encryption requests to generate a distributed encryption scheme) implemented by the core server of the present embodiment.
Fig. 1 is a flowchart of a distributed encryption method implemented by a core server according to this embodiment, and as shown in fig. 1, the method includes:
s101, obtaining a plurality of distributed encryption requests of the edge device, wherein each distributed encryption request comprises an evaluation index.
Specifically, the plurality of distributed encryption requests may be obtained by the gateway receiving information from the edge device in real time.
And S102, carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme.
S103, sending the distributed encryption scheme to the edge device for the edge device to encrypt according to the distributed encryption scheme.
Fig. 2 is a flowchart of a method for deep analysis and deep analysis evaluation according to this embodiment, and the deep analysis and deep analysis evaluation idea of the present invention is to determine and analyze a distributed encryption request to generate a distributed encryption scheme that meets an evaluation index in the distributed encryption request. The deep analysis of the embodiment combines a multilayer convolution neuron, quantum key distribution and a deep unsupervised learning strategy method to realize distributed encryption with low delay, low computational power consumption and high encryption efficiency.
As shown in fig. 2, the depth analysis and the depth analysis evaluation specifically include the following steps:
and S1021, starting a new iteration loop, clearing the iteration times, setting the maximum iteration times, and setting the iteration parameters of the initial iteration according to the evaluation indexes in the distributed encryption requests.
The maximum number of iterations may be set as needed, and may be specifically 50. If the value of the maximum number of iterations is set too small, it will be inaccurate, and if it is set too large, it is computationally expensive.
When the evaluation indexes are encryption efficiency, delay rate and computational power consumption, the initial parameters are
Figure BDA0002792574560000081
I.e. the encryption efficiency in the evaluation index,
Figure BDA0002792574560000082
the delay rate in the evaluation index is,
Figure BDA0002792574560000083
the calculation power consumption in the evaluation index is obtained.
Wherein, i is 1,2, … m, j is 1,2, … n, t is 1,2, …, p, m is the maximum value of all values of i, n is the maximum value of all values of j, p is the maximum value of all values of t, and m n p is the number of the acquired distributed encryption requests.
And S1022, analyzing iterative parameters by using a multilayer convolution neuron, quantum key distribution and a deep unsupervised learning strategy, and generating a distributed encryption scheme and iterative parameters of next iteration.
In the process of each iteration, the strategy ideas of multilayer convolution neurons, quantum key distribution and deep unsupervised learning strategies are as follows: in a multidimensional space, a plurality of distributed encryption schemes migrate to the direction determined by the optimization task priority scheme according to strategy modes such as multilayer convolution neurons, quantum key distribution, deep unsupervised learning and the like, and parameters are iterated, namely
Figure BDA0002792574560000084
After input, the iteration parameters of the corresponding next iteration are output after multilayer convolution neurons, quantum key distribution and deep unsupervised learning analysis.
Specifically, the iterative parameters are analyzed by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy, and a distributed encryption scheme is generated, wherein the distributed encryption scheme is generated by using an optimization function;
the optimization function at the kth iteration is:
Figure BDA0002792574560000085
Skey＝H{Random[q Mod w]},q∈[1,2,…,+∞],w≤Ω，
Rkey＝H{Random[ρMod w]},ρ∈[1,2,…,+∞],w≤Ω，
Figure BDA0002792574560000086
Figure BDA0002792574560000087
wherein H is a hash function, Random is a function for generating Random numbers, text is data to be encrypted,
Figure BDA0002792574560000091
representing an exclusive or operation.
Figure BDA0002792574560000092
And
Figure BDA0002792574560000093
the condition of the segment verification (dividing Rkey into a plurality of conditions same as the number of q bits) is
Figure BDA0002792574560000094
Skey is the sender random key (of length q, used to encrypt data) corresponding to the remainder of division by some number w no greater than the hash table length.
Rkey is a receiver random key (with length ρ for encrypting data) corresponding to the remainder of division by a number w not greater than the hash table length.
Analyzing iterative parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy to generate iterative parameters of next iteration, wherein the iterative parameters of next iteration are generated by using a supervision function;
the supervision function at the kth iteration is:
Figure BDA0002792574560000095
Figure BDA0002792574560000096
Figure BDA0002792574560000097
wherein the content of the first and second substances,
Figure BDA0002792574560000098
AGmaxfor maximum encryption efficiency, EGminIs the minimum delay rate, CGminFor minimum computational power consumption, mod is the remainder operation.
And S1023, judging whether the iteration number reaches a threshold value, if so, ending the cycle, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
And S1024, if not, evaluating the distributed encryption scheme obtained by the iteration according to the evaluation function, and adding 1 to the iteration number and returning to the step S1022 under the condition that the distributed encryption scheme obtained by the iteration does not meet the evaluation function.
Wherein, the evaluation function in the k iteration is as follows:
Figure BDA0002792574560000101
where P represents a probability.
And S1025, under the condition that the distributed encryption scheme obtained by the iteration meets the evaluation function, ending the loop, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
Based on the evaluation function and the optimization function, when the evaluation function is not satisfied, the iterative parameters are analyzed by using a multi-layer convolution neuron, quantum key distribution and deep unsupervised learning strategy, so that the iterative parameters and the generated distributed encryption scheme are shifted to the optimization direction, and distributed encryption with low delay, low computational power consumption and high encryption efficiency is realized.
It should be noted that while the operations of the method of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Having described the method of the exemplary embodiment of the present invention based on the same inventive concept, the distributed encryption apparatus of the exemplary embodiment of the present invention will be described next with reference to fig. 3. The implementation of the device can be referred to the implementation of the method, and repeated details are not repeated. The terms "module" and "unit", as used below, may be software and/or hardware that implements a predetermined function. While the modules described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 3 is a block diagram schematically illustrating a distributed encryption apparatus according to an embodiment of the present invention, and as shown in fig. 3, the apparatus includes: the device comprises an acquisition module, an analysis module and a sending module.
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of distributed encryption requests of edge equipment, and each distributed encryption request comprises an evaluation index; the analysis module is used for carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme; and the sending module is used for sending the distributed encryption scheme to the edge equipment so that the edge equipment can encrypt the distributed encryption scheme.
Optionally, fig. 4 is a schematic block diagram of a composition of an analysis module provided in an embodiment of the present invention, and as shown in fig. 4, the analysis module includes: the device comprises an initial unit, an analysis unit, a first judgment unit and a second judgment unit.
The initial unit is used for resetting the iteration times when a new iteration cycle starts, setting the maximum iteration times and setting the iteration parameters of initial iteration according to the evaluation indexes in the distributed encryption requests;
the analysis unit is used for analyzing the iteration parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy and generating a distributed encryption scheme and iteration parameters of next iteration;
the first judgment unit is used for judging whether the iteration times reach a threshold value, if so, the circulation is ended, and the distributed encryption scheme obtained by the iteration is output as the distributed encryption scheme sent to the edge equipment;
the second judgment unit is used for evaluating the distributed encryption scheme obtained by the iteration according to the evaluation function when the first judgment unit judges that the iteration frequency does not reach the threshold value, and adding 1 to the iteration frequency under the condition that the distributed encryption scheme obtained by the iteration does not meet the evaluation function; and under the condition that the distributed encryption scheme obtained by the iteration meets the evaluation function, ending the loop, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
Further optionally, in the k-th iteration, the iteration parameter includes encryption efficiency
Figure BDA0002792574560000111
Rate of delay
Figure BDA0002792574560000112
Computing power consumption
Figure BDA0002792574560000113
Wherein i is 1,2, … m,
j＝1,2,…n，
t＝1,2,…,p，
m is the maximum value of all values of i, n is the maximum value of all values of j, and p is the maximum value of all values of t.
Further optionally, the evaluation function at the k-th iteration is:
Figure BDA0002792574560000121
where P represents a probability.
Further optionally, analyzing iterative parameters by using a multilayer convolution neuron, quantum key distribution and a deep unsupervised learning strategy, and generating a distributed encryption scheme, including generating the distributed encryption scheme by using an optimization function;
the optimization function at the kth iteration is:
Figure BDA0002792574560000122
Skey＝H{Random[q Mod w]},q∈[1,2,…,+∞],w≤Ω，
Rkey＝H{Random[ρMod w]},ρ∈[1,2,…,+∞],w≤Ω，
Figure BDA0002792574560000123
Figure BDA0002792574560000124
wherein H is a hash function, Random is a function for generating Random numbers, text is data to be encrypted,
Figure BDA0002792574560000125
representing an exclusive or operation.
Further optionally, analyzing iteration parameters by using a multilayer convolution neuron, quantum key distribution and a deep unsupervised learning strategy to generate iteration parameters of next iteration, wherein the generation of the iteration parameters of the next iteration by using a supervision function is included;
the supervision function at the kth iteration is:
Figure BDA0002792574560000126
Figure BDA0002792574560000127
Figure BDA0002792574560000131
wherein the content of the first and second substances,
Figure BDA0002792574560000132
AGmaxfor maximum encryption efficiency, EGminIs the minimum delay rate, CGminFor minimum computational power consumption, mod is the remainder operation.
Furthermore, although several modules of the distributed encryption apparatus are mentioned in the above detailed description, such division is not mandatory only. Indeed, the features and functions of two or more of the units described above may be embodied in one unit, according to embodiments of the invention. Also, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.

Claims (10)

1. A distributed encryption method, the method comprising:
acquiring a plurality of distributed encryption requests of edge equipment, wherein each distributed encryption request comprises an evaluation index;
carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme;
and sending the distributed encryption scheme to edge equipment so that the edge equipment can encrypt according to the distributed encryption scheme.
2. The method according to claim 1, wherein performing deep analysis and deep analysis evaluation on evaluation indexes in the plurality of distributed encryption requests to generate a distributed encryption scheme comprises:
starting a new iteration loop, resetting the iteration times, setting the maximum iteration times and setting the iteration parameters of initial iteration according to evaluation indexes in the distributed encryption requests;
analyzing iterative parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy, and generating a distributed encryption scheme and iterative parameters of next iteration;
judging whether the iteration times reach a threshold value, if so, ending the circulation, and outputting a distributed encryption scheme obtained by the iteration as a distributed encryption scheme sent to the edge equipment;
if not, evaluating the distributed encryption scheme obtained by the iteration according to an evaluation function, and under the condition that the distributed encryption scheme obtained by the iteration does not meet the evaluation function, adding 1 to the iteration number, returning to the step of analyzing the iteration parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy, and generating the distributed encryption scheme and the iteration parameters of the next iteration;
and under the condition that the distributed encryption scheme obtained by the iteration meets the evaluation function, ending the loop, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
3. The method of claim 2,
at the k iteration, the iteration parameter comprises encryption efficiency
Figure FDA0002792574550000021
Rate of delay
Figure FDA0002792574550000022
Computing power consumption
Figure FDA0002792574550000023
Wherein the content of the first and second substances,
i＝1,2,…m，
j＝1,2,…n，
t＝1,2,…,p，
m is the maximum value of all values of i, n is the maximum value of all values of j, and p is the maximum value of all values of t.
4. The method of claim 3,
the evaluation function at the kth iteration is:
Figure FDA0002792574550000024
where P represents a probability.
5. The method of claim 4, wherein analyzing iterative parameters with a multi-layer convolutional neuron, quantum key distribution, deep unsupervised learning strategy, and generating a distributed encryption scheme comprises generating a distributed encryption scheme with an optimization function;
the optimization function at the kth iteration is:
Figure FDA0002792574550000025
Skey＝H{Random[q Mod w]},q∈[1,2,…,+∞],w≤Ω，
Rkey＝H{Random[ρMod w]},ρ∈[1,2,…,+∞],w≤Ω，
Figure FDA0002792574550000026
Figure FDA0002792574550000027
wherein H is a hash function, Random is a function for generating Random numbers, text is data to be encrypted,
Figure FDA0002792574550000028
representing an exclusive or operation.
6. The method of claim 4, wherein analyzing the iterative parameters with a multi-layer convolutional neuron, quantum key distribution, and deep unsupervised learning strategy to generate iterative parameters for a next iteration, including generating iterative parameters for a next iteration with a supervision function;
the supervision function at the kth iteration is:
Figure FDA0002792574550000031
Figure FDA0002792574550000032
Figure FDA0002792574550000033
wherein the content of the first and second substances,
Figure FDA0002792574550000034
AGmaxfor maximum encryption efficiency, EGminIs the minimum delay rate, CGminFor minimum computational power consumption, mod is the remainder operation.
7. A distributed encryption apparatus, the apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of distributed encryption requests of edge equipment, and each distributed encryption request comprises an evaluation index;
the analysis module is used for carrying out deep analysis and deep analysis evaluation on the evaluation indexes in the distributed encryption requests to generate a distributed encryption scheme;
and the sending module is used for sending the distributed encryption scheme to the edge equipment so that the edge equipment can encrypt the distributed encryption scheme.
8. The method of claim 7, wherein the analysis module comprises:
the initial unit is used for resetting the iteration times when a new iteration cycle starts, setting the maximum iteration times and setting the iteration parameters of initial iteration according to the evaluation indexes in the distributed encryption requests;
the analysis unit is used for analyzing the iteration parameters by using a multilayer convolution neuron, quantum key distribution and deep unsupervised learning strategy and generating a distributed encryption scheme and iteration parameters of next iteration;
the first judgment unit is used for judging whether the iteration times reach a threshold value, if so, the circulation is ended, and the distributed encryption scheme obtained by the iteration is output as the distributed encryption scheme sent to the edge equipment;
the second judgment unit is used for evaluating the distributed encryption scheme obtained by the iteration according to the evaluation function when the first judgment unit judges that the iteration frequency does not reach the threshold value, and adding 1 to the iteration frequency under the condition that the distributed encryption scheme obtained by the iteration does not meet the evaluation function;
and under the condition that the distributed encryption scheme obtained by the iteration meets the evaluation function, ending the loop, and outputting the distributed encryption scheme obtained by the iteration as the distributed encryption scheme sent to the edge equipment.
9. The apparatus of claim 8,
at the k iteration, the iteration parameter comprises encryption efficiency
Figure FDA0002792574550000041
Rate of delay
Figure FDA0002792574550000042
Computing power consumption
Figure FDA0002792574550000043
Wherein the content of the first and second substances,
i＝1,2,…m，
j＝1,2,…n，
t＝1,2,…,p，
m is the maximum value of all values of i, n is the maximum value of all values of j, and p is the maximum value of all values of t.
10. The apparatus of claim 9,
the evaluation function at the kth iteration is:
Figure FDA0002792574550000051
where P represents a probability.
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