CN117874820A - Information multi-section confusion desensitization method and system based on consensus mechanism - Google Patents
Information multi-section confusion desensitization method and system based on consensus mechanism Download PDFInfo
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Abstract
The invention discloses an information multi-section confusion desensitization method and system based on a consensus mechanism, which relate to the technical field of confusion desensitization, wherein the method comprises the following steps: establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string; performing segment segmentation of a self-adaptive size to generate N segment segmentation results; calculating corresponding source data reward values, and generating reward value confusion selection results; executing fitness probability selection analysis of N segment segmentation results to generate probability selection results; performing confusion desensitization on the random encryption data string, and reconstructing the random encryption data string; the corresponding N nodes are configured, N node consensus verification is executed, and sequential distributed storage is carried out according to the consensus verification result, so that the problem that the information desensitization effect is poor due to the fact that the information desensitization work cannot adapt to the processing requirement under the distributed environment in the prior art is solved, and effective protection on sensitive information is realized.
Description
Technical Field
The invention relates to the technical field of confusion desensitization, in particular to an information multi-section confusion desensitization method and system based on a consensus mechanism.
Background
With the rapid development of information technology and the arrival of large data age, the problems of data security and privacy protection are increasingly emphasized. Among the many data, protection of sensitive information is particularly important, such as personal privacy, financial data, and the like. In order to prevent leakage of such sensitive information, it is necessary to subject such information to desensitization processing. The traditional information desensitization methods mainly comprise replacement, deletion, randomization and the like, but the methods have certain limitations, such as incapability of realizing multi-section confusion, incapability of ensuring that the information after confusion still has consistency, incapability of ensuring that the information after confusion still has usability and the like, and meanwhile, in a big data environment, the data processing is often carried out on a plurality of nodes, and the traditional information desensitization method cannot adapt to the processing requirements in the distributed environment.
The problem that the information desensitization effect is poor due to the fact that the information desensitization work in the prior art cannot adapt to the processing requirements in a distributed environment, so that sensitive information cannot be effectively protected finally.
Disclosure of Invention
The application provides an information multi-section confusion desensitization method and system based on a consensus mechanism, which solve the problem that the information desensitization effect is poor due to the fact that the information desensitization work cannot adapt to the processing requirements under a distributed environment in the prior art, and realize effective protection on sensitive information.
In view of the above, the present application provides a method for multi-segment confusion desensitization of information based on consensus mechanisms.
In a first aspect, the present application provides a method for multi-segment confusion desensitization of information based on a consensus mechanism, the method comprising: establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string, wherein the random encryption data string and source data have mapping identifications; performing self-adaptive size segment segmentation on the random encryption data string to generate N segment segmentation results; performing source data reward value calculation corresponding to the N fragment segmentation results through the mapping identification, and generating reward value confusion selection results; executing fitness probability selection analysis of N segment segmentation results to generate probability selection results; performing confusion desensitization on the random encryption data string based on the reward value confusion selection result and the probability selection result, and reconstructing the random encryption data string; and configuring N nodes corresponding to the reconstructed random encryption data string, respectively executing N node consensus verification through a consensus mechanism, and performing sequential distributed storage according to a consensus verification result.
In a second aspect, the present application provides an information multi-segment confusion desensitization system based on a consensus mechanism, the system comprising: and a data random encryption module: establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string, wherein the random encryption data string and source data have mapping identifications; and a segment segmentation result module: performing self-adaptive size segment segmentation on the random encryption data string to generate N segment segmentation results; a source data prize value module: performing source data reward value calculation corresponding to the N fragment segmentation results through the mapping identification, and generating reward value confusion selection results; probability selection result module: executing fitness probability selection analysis of N segment segmentation results to generate probability selection results; an encrypted data string module: performing confusion desensitization on the random encryption data string based on the reward value confusion selection result and the probability selection result, and reconstructing the random encryption data string; node consensus verification module: and configuring N nodes corresponding to the reconstructed random encryption data string, respectively executing N node consensus verification through a consensus mechanism, and performing sequential distributed storage according to a consensus verification result.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the information multi-section confusion desensitization method based on the consensus mechanism when executing the executable instructions stored in the memory.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the method for multi-segment confusion desensitization of information based on a consensus mechanism provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the information multi-section confusion desensitization method and system based on the consensus mechanism, through establishing data communication with a user side, carrying out data random encryption of interaction data in a trusted execution environment, establishing a random encryption data string, carrying out self-adaptive size segment segmentation on the random encryption data string to generate N segment segmentation results, then carrying out source data reward value calculation corresponding to the N segment segmentation results through mapping identification, generating reward value confusion selection results, carrying out fitness probability selection analysis of the N segment segmentation results, generating probability selection results, carrying out confusion desensitization on the random encryption data string based on the reward value confusion selection results and the probability selection results, reconstructing the random encryption data string, finally configuring N nodes corresponding to the reconstructed random encryption data string, respectively carrying out N node consensus verification through the consensus mechanism, and carrying out sequential distributed storage, the problem that information desensitization work in the prior art is poor in information desensitization effect due to processing requirements under a distributed environment can not be met is solved, and effective protection on sensitive information is realized.
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FIG. 1 is a schematic flow diagram of a method for multi-segment confusion desensitization of information based on consensus mechanism;
fig. 2 is a schematic structural diagram of an information multi-segment confusion desensitization system based on a consensus mechanism.
Fig. 3 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the device comprises a data random encryption module 11, a fragment segmentation result module 12, a source data reward value module 13, a probability selection result module 14, an encrypted data string module 15, a node consensus verification module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
According to the method and the system for information multi-section confusion desensitization based on the consensus mechanism, data communication with a user side is established, data of interaction data are randomly encrypted in a trusted execution environment, a random encryption data string is established, self-adaptive size fragment segmentation is carried out on the random encryption data string, N fragment segmentation results are generated, source data reward value calculation corresponding to the N fragment segmentation results is carried out through mapping identification, reward value confusion selection results are generated, adaptive probability selection analysis of the N fragment segmentation results is carried out, probability selection results are generated, confusion desensitization of the random encryption data string is carried out based on the reward value confusion selection results and the probability selection results, the random encryption data string is reconstructed, N nodes corresponding to the reconstructed random encryption data string are finally configured, N node consensus verification is respectively carried out through the consensus mechanism, and sequential distributed storage is carried out. The problem that the information desensitization effect is poor due to the fact that the information desensitization work cannot adapt to the processing requirements in the distributed environment in the prior art is solved, and effective protection of sensitive information is achieved.
Example 1
As shown in fig. 1, the present application provides a method and a system for multi-segment confusion desensitization of information based on a consensus mechanism, wherein the method includes:
establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string, wherein the random encryption data string and source data have mapping identifications;
establish a secure communication channel with the user side and use an encrypted transport protocol including TLS/SSL, etc. to protect the security of data during transmission. And performs data processing and encryption operations in a trusted execution environment to ensure security and privacy protection of the data. The trusted execution environment may include a hardware security module and a trusted computing environment. By executing code and processing data in a trusted execution environment, malware and attackers are prevented from stealing and tampering with the data, and the data is randomly encrypted. The data is encrypted using a random encryption algorithm. The random encryption algorithm encrypts data by using a randomly generated key, so that each encryption result is different, and the cracking difficulty is increased. And establishing a mapping relation between the random encryption data string and the source data, carrying out mapping identification on the random encryption data string and the source data, and finding out corresponding data through the mapping identification so as to facilitate subsequent data processing and recovery operation.
Performing self-adaptive size segment segmentation on the random encryption data string to generate N segment segmentation results;
and determining the size of the fragments according to the size of the random encryption data string and the segmentation requirement, wherein when the random encryption data string is larger, each corresponding fragment is more, and each fragment size is correspondingly larger, namely the self-adaptive size cutting. The random encryption data string is divided according to the determined fragment size, and if the length of the random encryption data string is not divided by the fragment size, the last fragment can be processed using a padding manner. And generating N fragment segmentation results according to the segmented fragments, wherein each fragment segmentation result comprises the sequence number of the fragment, specific data content and other relevant information. And performing self-adaptive size fragment segmentation on the random encryption data string to provide a data basis for generating reward value confusion selection results by performing source data reward value calculation corresponding to N fragment segmentation results through mapping identification.
Performing source data reward value calculation corresponding to the N fragment segmentation results through the mapping identification, and generating reward value confusion selection results;
establishing a reward value evaluation network, wherein the reward value evaluation network comprises a basic reward value evaluation sub-network, a user evaluation sub-network and a normalization sub-network;
when the source data reward value calculation of N segment segmentation results is carried out, inputting the source data characteristics corresponding to the segment segmentation results into a reward value evaluation network;
performing reward value calculation of the source data characteristics through a basic reward value evaluation sub-network and a user evaluation sub-network respectively, synchronizing calculation results to a normalization sub-network, and performing weighted calculation;
outputting a reward value calculation result corresponding to the segment segmentation result according to the weighted calculation result;
and completing prize value confusion selection according to the prize value calculation result.
The reward value refers to a degree of value of data including a network channel for evaluating data with respect to itself as a base reward value evaluation sub-network based on big data and a network channel for evaluating data with respect to itself as a user evaluation sub-network based on user data. The base prize value evaluation subnetwork, the user evaluation subnetwork, and the normalized subnetwork are collectively constructed as a prize value evaluation network. The normalization sub-network is a sub-network for performing normalization processing on output values of the basic reward value evaluation sub-network and the user evaluation sub-network. And inputting the source data characteristics corresponding to the corresponding segment segmentation results into a reward value evaluation network to obtain the source data reward values of the N segment segmentation results. And respectively carrying out reward value calculation of the source data characteristics through a basic reward value evaluation sub-network and a user evaluation sub-network to respectively obtain basic reward value evaluation and user evaluation, inputting the basic reward value evaluation and the user evaluation into a normalization sub-network, and carrying out weighted calculation on calculation results of the basic reward value and the user evaluation by the normalization sub-network according to specific weight setting and weighting algorithm to obtain a final reward value calculation result. And performing confusion selection on the prize values of the fragment segmentation result by using an encryption algorithm or a confusion technology according to the prize value calculation result. And establishing a reward value evaluation network to carry out reward value confusion selection, so that a reward value confusion selection result which is more in line with the actual situation can be obtained.
Executing fitness probability selection analysis of N segment segmentation results to generate probability selection results;
establishing a probability selection function, wherein the probability selection function is constructed as follows:
wherein p is i Selection of segmentation results for the ith segmentThe medium probability, K is a constraint coefficient, F i Individual fitness values of the segmentation result for the ith segment;
and carrying out fitness probability selection analysis through the probability selection function to obtain a probability selection result.
Fitness probability refers to the probability that each individual or segment is selected in the entire collection. It is generally calculated according to fitness values of individuals or segments, and the higher the fitness value, the greater the probability of being selected, so the fitness probability selection analysis is a selection method based on fitness probability, and is used for selecting individuals or segments with higher fitness from a candidate set. And comparing the constraint coefficient with the individual fitness value of each segment segmentation result to obtain a plurality of comparison results, and summing the comparison results to obtain the sum of the comparison results. Comparing the individual fitness value of the 1 st segment segmentation result with the sum of a plurality of comparison results to obtain the selected probability of the 1 st segment segmentation result, obtaining the selected probability of all segment segmentation results by the same way, integrating the selected probabilities of all segment segmentation results to obtain a probability selected result, and providing a data basis for carrying out confusion desensitization on the random encryption data string based on the reward value confusion selected result and the probability selected result and reconstructing the random encryption data string.
Performing confusion desensitization on the random encryption data string based on the reward value confusion selection result and the probability selection result, and reconstructing the random encryption data string;
establishing a desensitization scheme database, and carrying out matching evaluation of the desensitization scheme database through corresponding N fragment segmentation results when reconstructing a random encryption data string;
and randomly selecting a desensitization scheme set meeting a preset matching evaluation result, and completing reconstruction of the random encrypted data string according to the random selection result.
And carrying out confusion desensitization on the random encryption data string according to the reward value confusion selection result and the probability selection result, and carrying out further analysis due to reconstruction on the random encryption data string. A database of desensitization schemes is first constructed based on big data for storing a plurality of desensitization schemes, each scheme comprising two parts: desensitization rules and matching evaluation results. The desensitization rules define how the data is desensitized, e.g., replaced, deleted, obscured, etc. The matching evaluation result is an evaluation of the suitability of the desensitization scheme, judging whether the desensitization scheme can be used. Each fragment is matched against the protocols in the database of desensitized protocols, which can be based on a variety of factors, such as data type, degree of matching, security, etc. And screening out a desensitization scheme set meeting the preset matching evaluation result according to the evaluation result. And randomly selecting a desensitization scheme set meeting a preset matching evaluation result, obtaining a random selection result, carrying out corresponding desensitization treatment according to a desensitization scheme in the random selection result, generating new desensitization data for each segment, combining the desensitization segments, and completing reconstruction of a random encryption data string. By establishing a desensitization scheme database to reconstruct the random encryption data string, the acquired better encryption scheme can be realized, and better data protection is further provided.
And configuring N nodes corresponding to the reconstructed random encryption data string, respectively executing N node consensus verification through a consensus mechanism, and performing sequential distributed storage according to a consensus verification result.
When N node consensus verifications are respectively executed, synchronously transmitting node data of corresponding nodes to M storage devices for node authentication;
if the authentication result of the M storage devices on the node data is a passing result, the consensus authentication is passed, the corresponding node is constructed as a new added block, and the new added block is stored in a distributed mode;
and performing iterative verification of the newly added block, and establishing a distributed storage block chain to finish distributed storage.
And synchronizing the data of each node to M storage devices for authentication through network transmission, so as to ensure that all the storage devices can obtain the complete data of the node. Node authentication refers to verifying the integrity, correctness, legality, and the like of data. And authenticating the node data on the M storage devices through an authentication algorithm and technology, so as to ensure the safety and consistency of the data. Judging whether the node data passes the consensus authentication according to the authentication result of the M storage devices on the node data, and if all the storage devices give the passing authentication result to the node data, constructing the node as a new block and performing distributed storage. Otherwise, the authentication is not passed, and error processing or consensus verification is required to be performed again. And constructing the nodes passing the consensus authentication as newly added blocks. Wherein the newly added block contains data of the node. And the newly added blocks are distributed and stored through a block chain technology, so that the non-falsifiability of data and the security of distributed storage are ensured. The newly added block can propagate in the network through the consensus algorithm and be accepted and stored by other nodes. And finally, carrying out iterative verification on the newly added block, wherein the iterative verification comprises the steps of verifying hash values, time stamps and other information of the newly added block, correlating with the previous block, ensuring the validity and correctness of the newly added block, and if the newly added block passes the verification, continuing the verification of the next block, and completing the distributed storage according to the consensus verification result. By synchronizing the node data to multiple storage devices for authentication, the security and reliability of the data may be increased.
Further, the method further comprises:
acquiring a preset encryption security value of a user;
summarizing all reward value calculation results, establishing a reward value set, carrying out proportion segmentation on the reward value set through the preset encryption security value, and positioning to obtain a proportion reward threshold;
and setting a preset reward threshold through big data, and carrying out data evaluation on a reward value set through the proportional reward threshold and the preset reward threshold to generate a reward value confusion selection result.
The preset encryption security value is that the user sets the encryption security value, obtains all prize value calculation results, gathers the prize value calculation results, and constructs a data set, namely a prize value set. And carrying out proportion segmentation of the reward value set through a preset encryption security value, namely carrying out encryption security value evaluation on the reward value set, obtaining an encryption security value evaluation result, namely dividing the reward value set into two results, positioning and obtaining a value in the middle of the two results, wherein the obtained value is a proportion reward threshold value, and the threshold value represents the encryption security degree. The method comprises the steps of obtaining a large number of reward thresholds through large data, screening the reward thresholds to obtain screening results, setting the screening results to be preset reward thresholds, carrying out data evaluation of a reward value set through the proportion reward thresholds and the preset reward thresholds together, and generating reward value confusion selection results. The prize value calculation result is further processed, and the preset encryption security value is added for analysis, so that the obtained prize value confusion selection result is more accurate.
Further, the method further comprises:
setting period verification constraint and trigger verification constraint;
when the period verification constraint and/or the trigger verification constraint are triggered, then performing consensus verification;
and carrying out consensus updating of the data through the response result of the distributed storage unit.
The periodic verification constraint refers to verifying and comparing the data of all the storage units in a set time interval, for example, if the data is inconsistent or has errors, corresponding processing or common-knowledge verification needs to be performed again. Triggering verification constraint means that when a specific condition triggers, data of a storage unit is verified and compared, for example, when a certain node is added or withdrawn from a network, consensus verification needs to be performed again; or when certain critical data is modified, corresponding verification and update operations are required. When the cycle verification constraint or the trigger verification constraint is triggered, the consensus verification needs to be performed. And only when most storage units give the same authentication result to the data, the consensus update of the data can be performed. The distributed storage unit is the unit for completing the distributed storage. And performing consensus updating of the data through a response result of the distributed storage unit, and writing the data passing the consensus authentication into a block chain or other distributed storage systems so as to ensure the reliability and consistency of the data. By setting the period verification constraint and triggering the verification constraint, the consistency and the reliability of the data of the distributed storage units can be ensured, so that the safety and the reliability of the whole system are improved.
Example two
Based on the same inventive concept as the information multi-segment confusion desensitization method based on the consensus mechanism in the foregoing embodiment, as shown in fig. 2, the present application provides an information multi-segment confusion desensitization system based on the consensus mechanism, the system includes:
the data random encryption module 11: the data random encryption module 11 is used for establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string, wherein the random encryption data string and source data have mapping identifications;
the segment segmentation result module 12: the segment segmentation result module 12 is configured to segment the random encrypted data string in a segment with an adaptive size, and generate N segment segmentation results;
source data prize value module 13: the source data reward value module 13 is configured to perform calculation of source data reward values corresponding to the N segment segmentation results according to the mapping identifier, and generate a reward value confusion selection result;
probability selection result module 14: the probability selection result module 14 is configured to perform fitness probability selection analysis of the N segment segmentation results, and generate a probability selection result;
the encrypted data string module 15: the encrypted data string module 15 is configured to perform confusion and desensitization on the random encrypted data string based on the prize value confusion selection result and the probability selection result, and reconstruct the random encrypted data string;
node consensus verification module 16: the method comprises the steps of configuring N nodes corresponding to the reconstructed random encryption data string, respectively executing N node consensus verification through a consensus mechanism, and carrying out sequential distributed storage according to a consensus verification result.
Further, the system further comprises:
prize value evaluation network establishment module: the rewards value evaluation network establishing module is used for establishing a rewards value evaluation network, and the rewards value evaluation network comprises a basic rewards value evaluation sub-network, a user evaluation sub-network and a normalization sub-network;
prize value evaluation network input module: the rewarding value evaluation network input module is used for inputting the source data characteristics corresponding to the segmentation results into the rewarding value evaluation network when the source data rewarding value calculation of the N segmentation results is carried out;
a base prize value evaluation sub-network module: the basic reward value evaluation sub-network module is used for carrying out reward value calculation of the source data characteristics through the basic reward value evaluation sub-network and the user evaluation sub-network respectively, synchronizing calculation results to the normalization sub-network and executing weighted calculation;
prize value calculation result output result module: the reward value calculation result output module is used for outputting a reward value calculation result corresponding to the segment segmentation result according to the weighted calculation result;
prize value obfuscation selection module: and the reward value confusion selecting module is used for completing reward value confusion selecting according to the reward value calculation result.
Further, the system further comprises:
a preset encryption security value acquisition module: the preset encryption security value acquisition module is used for acquiring a preset encryption security value of a user;
prize value calculation result summarization module: the reward value calculation result summarizing module is used for summarizing all reward value calculation results, establishing a reward value set, carrying out proportion segmentation on the reward value set through the preset encryption security value, and positioning to obtain a proportion reward threshold value;
a preset rewarding threshold setting module: the preset reward threshold setting module is used for setting a preset reward threshold through big data, and carrying out data evaluation on a reward value set through the proportional reward threshold and the preset reward threshold to generate a reward value confusion selection result.
Further, the system further comprises:
probability selection function establishment module: the probability selection function building module is used for building a probability selection function and is constructed as follows:
individual fitness value module: the individual fitness value module is used for p i For the selected probability of the i-th segment segmentation result, K is a constraint coefficient, F j Individual fitness values of the segmentation result for the ith segment;
fitness probability selection analysis module: and the fitness probability selection analysis module is used for carrying out fitness probability selection analysis through the probability selection function to obtain a probability selection result.
Further, the system further comprises:
node consensus verification module: the node consensus verification module is used for synchronously transmitting node data of corresponding nodes to M storage devices for node authentication when N node consensus verifications are respectively executed;
newly added block distributed storage modules: the newly added block distributed storage module is used for constructing a corresponding node as a newly added block and storing the newly added block in a distributed mode if the authentication result of the M storage devices on the node data is a passing result and the consensus authentication is passed;
the distributed storage block chain building module: the distributed storage block chain establishment module is used for performing iterative verification of the newly added block and establishing a distributed storage block chain so as to finish distributed storage.
Further, the system further comprises:
desensitization scheme database creation module: the desensitization scheme database establishing module is used for establishing a desensitization scheme database, and when the random encryption data string is reconstructed, matching evaluation of the desensitization scheme database is carried out through the corresponding N fragment segmentation results;
desensitization scheme set random selection module: the desensitization scheme set random selection module is used for randomly selecting the desensitization scheme set meeting the preset matching evaluation result, and completing reconstruction of the random encrypted data string according to the random selection result.
Further, the system further comprises:
and the period verification constraint setting module is used for: the period verification constraint setting module is used for setting period verification constraints and triggering verification constraints;
and the consensus verification execution module is used for: the consensus verification executing module is used for executing consensus verification when the period verification constraint and/or the trigger verification constraint are/is triggered;
and the data consensus updating module is used for: and the data consensus updating module is used for carrying out consensus updating on the data through the response result of the distributed storage unit.
The foregoing detailed description of the method for multi-segment confusion and desensitization of information based on the consensus mechanism will be clear to those skilled in the art, and the system disclosed in this embodiment is relatively simple in description, and the relevant points refer to the method part for description, because it corresponds to the method disclosed in the embodiment.
Example III
Fig. 3 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 3, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 3, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 3, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the method for multi-segment confusion-desensitization of information based on consensus mechanisms in embodiments of the present invention. The processor 31 executes various functional applications of the computer device and data processing, i.e. implements the above-described method of information multi-segment confusion-desensitization based on consensus mechanisms, by running software programs, instructions and modules stored in the memory 32.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The information multi-section confusion desensitization method based on consensus mechanism is characterized by comprising the following steps:
establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string, wherein the random encryption data string and source data have mapping identifications;
performing self-adaptive size segment segmentation on the random encryption data string to generate N segment segmentation results;
performing source data reward value calculation corresponding to the N fragment segmentation results through the mapping identification, and generating reward value confusion selection results;
executing fitness probability selection analysis of N segment segmentation results to generate probability selection results;
performing confusion desensitization on the random encryption data string based on the reward value confusion selection result and the probability selection result, and reconstructing the random encryption data string;
and configuring N nodes corresponding to the reconstructed random encryption data string, respectively executing N node consensus verification through a consensus mechanism, and performing sequential distributed storage according to a consensus verification result.
2. The method of claim 1, wherein the method further comprises:
establishing a reward value evaluation network, wherein the reward value evaluation network comprises a basic reward value evaluation sub-network, a user evaluation sub-network and a normalization sub-network;
when the source data reward value calculation of N segment segmentation results is carried out, inputting the source data characteristics corresponding to the segment segmentation results into a reward value evaluation network;
performing reward value calculation of the source data characteristics through a basic reward value evaluation sub-network and a user evaluation sub-network respectively, synchronizing calculation results to a normalization sub-network, and performing weighted calculation;
outputting a reward value calculation result corresponding to the segment segmentation result according to the weighted calculation result;
and completing prize value confusion selection according to the prize value calculation result.
3. The method of claim 2, wherein the method further comprises:
acquiring a preset encryption security value of a user;
summarizing all reward value calculation results, establishing a reward value set, carrying out proportion segmentation on the reward value set through the preset encryption security value, and positioning to obtain a proportion reward threshold;
and setting a preset reward threshold through big data, and carrying out data evaluation on a reward value set through the proportional reward threshold and the preset reward threshold to generate a reward value confusion selection result.
4. The method of claim 1, wherein performing an adaptive probability check analysis of the N segment segmentation results generates probability check results, further comprising:
establishing a probability selection function, wherein the probability selection function is constructed as follows:
wherein p is i For the selected probability of the i-th segment segmentation result, K is a constraint coefficient, F i Individual fitness values of the segmentation result for the ith segment;
and carrying out fitness probability selection analysis through the probability selection function to obtain a probability selection result.
5. The method of claim 1, wherein the method further comprises:
when N node consensus verifications are respectively executed, synchronously transmitting node data of corresponding nodes to M storage devices for node authentication;
if the authentication result of the M storage devices on the node data is a passing result, the consensus authentication is passed, the corresponding node is constructed as a new added block, and the new added block is stored in a distributed mode;
and performing iterative verification of the newly added block, and establishing a distributed storage block chain to finish distributed storage.
6. The method of claim 1, wherein the method further comprises:
establishing a desensitization scheme database, and carrying out matching evaluation of the desensitization scheme database through corresponding N fragment segmentation results when reconstructing a random encryption data string;
and randomly selecting a desensitization scheme set meeting a preset matching evaluation result, and completing reconstruction of the random encrypted data string according to the random selection result.
7. The method of claim 1, wherein the method further comprises:
setting period verification constraint and trigger verification constraint;
when the period verification constraint and/or the trigger verification constraint are triggered, then performing consensus verification;
and carrying out consensus updating of the data through the response result of the distributed storage unit.
8. A consensus mechanism based information multi-segment confusion desensitization system, the system comprising:
and a data random encryption module: establishing data communication with a user side, carrying out data random encryption of interactive data in a trusted execution environment, and establishing a random encryption data string, wherein the random encryption data string and source data have mapping identifications;
and a segment segmentation result module: performing self-adaptive size segment segmentation on the random encryption data string to generate N segment segmentation results;
a source data prize value module: performing source data reward value calculation corresponding to the N fragment segmentation results through the mapping identification, and generating reward value confusion selection results;
probability selection result module: executing fitness probability selection analysis of N segment segmentation results to generate probability selection results;
an encrypted data string module: performing confusion desensitization on the random encryption data string based on the reward value confusion selection result and the probability selection result, and reconstructing the random encryption data string;
node consensus verification module: and configuring N nodes corresponding to the reconstructed random encryption data string, respectively executing N node consensus verification through a consensus mechanism, and performing sequential distributed storage according to a consensus verification result.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing the consensus mechanism based information multi-segment confusion desensitization method according to any one of claims 1 to 7 when executing executable instructions stored in the memory.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the consensus mechanism based information multi-segment confusion desensitization method according to any of claims 1 to 7.
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