Data compliance privacy protection method based on block chain

Abstract

The invention discloses a data compliance privacy protection method based on a block chain, and the method comprises the following steps: S1, building a block chain network, and uploading original data; s2, classifying original data, marking sensitive data and encrypting the sensitive data; s3, dynamically distributing authority, and adjusting authority rules in combination with an immune mechanism; s4, completing permission condition verification, and storing a verification result and a log to the block chain network; s5, optimizing an auditing strategy, generating an auditing task and allocating resources; s6, cooperatively completing the auditing task, generating a global verification result and recording the global verification result; s7, adjusting an authority verification and auditing process, and generating a compliance report; and S8, recording a complete permission verification and auditing process. According to the method, the block chain technology is combined with dynamic authority management, privacy multi-party calculation and multi-scale modeling, so that data privacy protection, compliance management and high efficiency, safety and transparency of cross-regional collaboration are realized.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a data compliance and privacy protection method based on blockchain. Background Art

[0002] In the context of the rapid development of information technology, data privacy protection and compliance management have become core challenges faced by various industries. With the rise of blockchain technology, its characteristics of decentralization, transparency and immutability have been widely used in the field of data management. However, traditional blockchain technology still has significant technical defects in privacy protection and compliance management, and it is difficult to fully meet the needs of modern complex environments.

[0003] In the traditional permission allocation and auditing process, permission rules are usually defined statically, and user access requests are verified based on pre-set rules. This static approach is difficult to adapt to dynamic permission management needs. For example, in data access scenarios involving multiple users and multiple permission levels, changes in user permissions may be real-time, and static rules often cannot be quickly adjusted to meet such changes. Especially in the face of malicious attacks or abnormal behaviors, static rules cannot respond flexibly, which may lead to security vulnerabilities in permission management. In addition, the traditional permission allocation mechanism relies on fixed verification logic, which makes it difficult to accurately detect and prevent potential abnormal access in complex scenarios.

[0004] The security of data encryption and storage is another urgent problem to be solved. In the existing technology, sensitive data is usually protected by static encryption. Although it can prevent unauthorized access to a certain extent, the leakage of static encryption keys will cause the data to face huge risks. In addition, the security of data in a distributed storage environment is also difficult to be fully guaranteed. Attackers may obtain data content by frequently accessing or parsing the communication traffic of distributed storage nodes. Especially in the blockchain system, although the on-chain data is tamper-proof, the sensitive data stored off-chain often becomes the weak link of the system.

[0005] The low efficiency and high cost of compliance audits are also a major pain point of existing technologies. The traditional compliance audit process mainly relies on manual or semi-automated methods, which require checking the matching of permission rules and access behaviors one by one. This method is not only time-consuming and inefficient, but also requires a lot of resources. Especially in cross-regional compliance collaboration, due to the significant differences in regulations in various regions, it is difficult for traditional audit methods to quickly unify the compliance verification standards of each node, resulting in difficulties in compliance verification during cross-regional collaboration. In addition, it is difficult for existing audit tools to intelligently prioritize audits of high-risk data or behaviors, which may delay the discovery and handling of key issues.

[0006] Traditional blockchain technology also has significant deficiencies in data privacy protection. Although blockchain has certain privacy enhancement capabilities (such as zero-knowledge proof and privacy transaction technology), these technologies are usually limited to on-chain operations, and the privacy protection needs of off-chain data are still difficult to meet. In addition, existing blockchain technology has limited capabilities in dynamic permission verification, audit optimization, and multi-party collaboration, making it difficult to meet the comprehensive needs of privacy protection and compliance in complex environments.

[0007] Therefore, how to provide a blockchain-based data compliance and privacy protection method is an urgent problem that technicians in this field need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a data compliance and privacy protection method based on blockchain. The present invention fully combines biological inspiration mechanisms, multi-objective optimization algorithms and blockchain technology, and describes in detail a technical solution for realizing cross-regional verification collaboration by dynamically adjusting authority verification rules, optimizing audit strategies and combining privacy multi-party computing. It has the advantages of strong data privacy protection capabilities, high flexibility in authority management, high compliance audit efficiency and strong adaptability to cross-regional collaboration.

[0009] A data compliance and privacy protection method based on blockchain according to an embodiment of the present invention includes the following steps: S1. Build a blockchain network, and the nodes upload the original data to the blockchain network as the data to be protected; S2. Classify the data to be protected, generate encryption keys using a pulse neural network, and record data summaries, encryption metadata, and access permission rules on the blockchain network; S3, combining access permission rules and encrypted metadata, using a dynamic allocation mechanism inspired by the immune system, using the antigen-antibody matching principle to verify user access requests, and dynamically adjusting permission rules through an immune memory mechanism; S4. Based on the completed permission verification results, use zero-knowledge proof technology to verify whether the user meets the access conditions and complete the permission condition verification; S5. Combine the results of the permission rules and permission conditions verification, use the multi-objective reinforcement learning model to adjust the audit strategy and generate audit tasks; determine the audit task priority through the reward function, and optimize the storage path and resource allocation in combination with the genetic algorithm; S6. For the generated audit tasks, we use privacy-preserving multi-party computing technology to process the audit tasks, perform verification collaboratively between nodes, and synthesize the global verification results. S7. Adjust the permission verification and audit process based on multi-scale mathematical modeling through global verification results, permission verification logs and permission rules, apply cell-level mechanisms to single request processing, and apply group-level mechanisms to the global audit process to generate compliance reports; S8. Combined with the compliance report, the complete permission verification and audit process is recorded on the blockchain network through the regulatory node.

[0010] Optionally, the S2 specifically includes: S21. Obtain the uploaded data to be protected, classify it based on data features, and distinguish between sensitive data and non-sensitive data; S22. Attach a privacy label to the sensitive data, where the privacy label includes a sensitivity level identifier and access permission rules; S23. Encode the data set marked as sensitive data and use the spiking neural network to generate discrete pulse signals for the sensitive data: ; in, is the membrane potential at time t, is the activation function, is the time decay function, is the time decay factor, i is the index of the neuron, is the neuron output signal of the spiking neural network; S24. Dynamically adjust synaptic weights based on the pulse neural network and generate encryption keys: ; in, is the dynamic encryption key generated at time t, H is the hash function, is the synaptic weight between the ith neuron and the jth neuron at time t, is the learning rate, Indicates time The pulse output signal of the jth neuron at time instant; S25. Encrypt the sensitive data using the dynamically generated encryption key, and store the encrypted data in the distributed storage network; S26. Record summary information, dynamic encryption metadata, and access permission rules for sensitive data on the blockchain network.

[0011] Optionally, the S3 specifically includes: S31, extracting access permission rules and encrypted metadata stored in the blockchain network, and parsing the data identifier in the access request submitted by the user; S32. Use the dynamic permission allocation mechanism inspired by the immune system to match user requests and permission rules according to the antigen-antibody matching principle: ; in, Represents user request u and permission rules The matching strength, represents the feature vector of user request u, Represents permission rules The eigenvector of , exp is the natural exponential function, is a hyperparameter that controls the width of the function; S33, verify whether the user request meets the access permission rules, record the matching result as an access verification log, and store it in the blockchain network; S34, extracting historical access logs from the blockchain network and analyzing the correlation between user behavior patterns and current access requests; S35. Based on the immune memory mechanism, dynamically adjust the priority and adaptability of access permission rules in combination with historical access behaviors: ; in, represents the weight of the access permission rule at time t, is the learning rate, is the dynamic correlation change of the access permission rule, is the weight of the access permission rule at time t+1, is the weight adjustment rate of the immune memory mechanism, It is the long-term memory value of the access permission rule; S36. Synchronously update the adjusted access permission rules and matching results to the blockchain network.

[0012] Optionally, the S4 specifically includes: S41, parsing the access target and permission conditions in the user request through the user access request and the access permission rule set; S42. Construct verification problems based on zero-knowledge proof technology to convert user requests into permission verification problems without leaking user sensitive information during the verification process; S43. Verify whether the user meets the access conditions by using zero-knowledge proof technology to complete the permission condition verification: ; in, is the verification result function, u is the access request submitted by the user, is a logical statement of a set of permission rules. Indicates the matching strength between the user request and the permission rule. is the permission rule, I is the indicator function, and m is the total number of permission rules; S44. When the verification is successful, the matching success status and the identifier of the corresponding permission rule are recorded; when the verification fails, the failure reason is recorded and a permission verification log is generated; S45. Store the permission condition verification result and the generated permission verification log in the blockchain network, including the user request summary and the matching rule identifier.

[0013] Optionally, the S5 specifically includes: S51, extracting permission logs and access records from the blockchain network, combining the permission rules and permission condition verification results, and generating a basic data set; S52. Construct a multi-objective reinforcement learning model based on the basic data set. The state of the model includes the permission state and access behavior, the action includes adjusting the audit strategy and task allocation, and the reward value reflects the optimization goals of audit efficiency, compliance and risk; S53. Use the multi-objective reinforcement learning model to analyze permission logs and access records, dynamically adjust the audit strategy based on the current status, and generate audit tasks: ; in, represents the optimal strategy under the current state, represents the current state, A represents the set of all optional actions in the model, a represents the action, Indicates in status The expected cumulative reward after taking action a, is the discount factor, Indicates the next state, Indicates that from the current state After taking action a, transfer to the next state The probability of Indicates the next state The reward value obtained when , argmax represents the action a used to find the maximum value in the brackets; S54. Prioritize audit tasks by reward value, and give priority to auditing data with higher risks; S55. Combine genetic algorithm to optimize the storage path and resource allocation scheme of audit tasks: ; ; in, is the comprehensive optimization objective function, x is the audit task execution plan, is the weight coefficient, Score the audit task priority, Score storage paths and execution efficiency, is the resource consumption cost, represents the next generation solution after genetic algorithm optimization, is a parent generation scheme of the current generation, Another parent generation scheme for the current generation, CrossOver() represents the crossover operation, and Mutate() represents the mutation operation; S56. Complete the audit task according to the optimized audit strategy, and store the audit results and optimization records in the blockchain network, including the audit result summary and resource allocation log.

[0014] Optionally, the S6 specifically includes: S61. For the generated audit tasks, the tasks are distributed to multiple nodes. Each node processes the local tasks independently and generates local verification results according to the local authority rules and verification logs. S62. Using privacy multi-party computing technology, each node shares the verification intermediate value through a security protocol without leaking the original data, completing the secure aggregation of local verification results: ; in, represents the global verification result, n represents the total number of nodes participating in the verification task, i represents the node index, is the node weight factor, g is the correction function, is the local verification result of node i, is the difference characteristic value of node i; S63. Based on the collaborative execution verification among nodes, synthesize the global verification results, which include cross-node data consistency and permission rule adaptability analysis; S64, recording the summary of the local verification result and the global verification result to the blockchain network; S65. Generate a report on the completion status of the audit task based on the global verification results recorded in the blockchain network, and associate the report with the verification records in the blockchain network.

[0015] Optionally, the S7 specifically includes: S71. Combining global verification results, permission verification logs and permission rules, a multi-scale mathematical modeling framework is constructed. The framework includes a cell-level mechanism and a group-level mechanism. The cell-level mechanism is used to process a single request, and the group-level mechanism is used to optimize the global audit process. S72. In the cell-level mechanism, the permission verification rules of a single request are dynamically adjusted according to the permission verification rules and matching status of the single request: ; in, is the adjusted authority verification weight, is the authority verification weight, is the rule matching degree change, is the adjustment factor; S73. In the group-level mechanism, based on the global verification results and permission verification logs, analyze the global data access pattern and optimize the task priority and resource allocation strategy in the audit process: ; in, is the priority of task j, is the risk factor of the rule, is the resource consumption weight, and is the weight coefficient; S74. Combine the cell-level optimization results and the population-level optimization results of the multi-scale model to adjust the authority verification process and the global audit strategy; S75. Generate a compliance report based on the optimized permission verification process and audit strategy, including permission adjustment records, audit result summary and rule optimization plan, and store the report in the blockchain network.

[0016] The beneficial effects of the present invention are: The data compliance and privacy protection method based on blockchain proposed in the present invention overcomes many technical difficulties existing in the prior art and significantly improves the ability of data privacy protection and compliance management. The present invention adopts a dynamic permission verification mechanism based on a biologically inspired dynamic adjustment model, which enables permission rules to quickly adapt to real-time changes in user requests, thereby improving the flexibility and security of permission management. At the same time, by introducing pulse neural networks to dynamically encrypt sensitive data and combining distributed storage technology, the security of data during transmission and storage is effectively improved, avoiding the risk of key leakage under static encryption.

[0017] In addition, the present invention optimizes the audit process and resource allocation strategy through multi-objective optimization algorithms and reinforcement learning models, so that high-risk data can be audited first, thereby improving the efficiency and accuracy of compliance audits and significantly reducing the cost of compliance management. By combining privacy multi-party computing technology, each node can collaboratively complete global verification without leaking original data, and generate a cross-regional compliance verification report, fundamentally solving the collaboration problem caused by cross-regional regulatory differences in the prior art. Finally, blockchain technology is used to record the entire verification and audit process, ensuring the transparency of the audit process, the immutability of data, and the traceability of results.

[0018] This invention combines the adjustment of single request permission rules with the optimization of global audit strategy through multi-scale mathematical modeling at the cell level and population level, realizes unified optimization from local to global, and provides an efficient, reliable and flexible solution for data privacy protection and compliance management. This method not only has broad application prospects in sensitive fields such as medicine, finance, and government affairs, but also provides technical support for multi-party collaboration and data sharing in complex regulatory environments, reflecting significant technical and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flowchart of a data compliance and privacy protection method based on blockchain proposed by the present invention; Figure 2 This is a schematic diagram of the sensitive data dynamic encryption and distributed storage architecture of the blockchain-based data compliance and privacy protection method proposed in the present invention. DETAILED DESCRIPTION

[0020] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0021] refer to Figure 1 and Figure 2 , a data compliance and privacy protection method based on blockchain, comprising the following steps: S1. Build a blockchain network, and the nodes upload the original data to the blockchain network as the data to be protected; S2. Classify the data to be protected, generate encryption keys using a pulse neural network, and record data summaries, encryption metadata, and access permission rules on the blockchain network; S3, combining access permission rules and encrypted metadata, using a dynamic allocation mechanism inspired by the immune system, using the antigen-antibody matching principle to verify user access requests, and dynamically adjusting permission rules through an immune memory mechanism; S4. Based on the completed permission verification results, use zero-knowledge proof technology to verify whether the user meets the access conditions and complete the permission condition verification; S5. Combine the results of the permission rules and permission conditions verification, use the multi-objective reinforcement learning model to adjust the audit strategy and generate audit tasks; determine the audit task priority through the reward function, and optimize the storage path and resource allocation in combination with the genetic algorithm; S6. For the generated audit tasks, we use privacy-preserving multi-party computing technology to process the audit tasks, perform verification collaboratively between nodes, and synthesize the global verification results. S7. Adjust the permission verification and audit process based on multi-scale mathematical modeling through global verification results, permission verification logs and permission rules, apply cell-level mechanisms to single request processing, and apply group-level mechanisms to the global audit process to generate compliance reports; S8. Combined with the compliance report, the complete permission verification and audit process is recorded on the blockchain network through the regulatory node.

[0022] In this implementation, S2 specifically includes: S21. Obtain the uploaded data to be protected, classify it based on data features, and distinguish between sensitive data and non-sensitive data; S22. Attach a privacy label to the sensitive data, where the privacy label includes a sensitivity level identifier and access permission rules; S23. Encode the data set marked as sensitive data and use the spiking neural network to generate discrete pulse signals for the sensitive data: ; in, is the membrane potential at time t, is the activation function, is the time decay function, is the time decay factor, i is the index of the neuron, is the neuron output signal of the spiking neural network; S24. Dynamically adjust synaptic weights based on the pulse neural network and generate encryption keys: ; in, is the dynamic encryption key generated at time t, H is the hash function, is the synaptic weight between the ith neuron and the jth neuron at time t, is the learning rate, Indicates time The pulse output signal of the jth neuron at time instant; S25. Encrypt the sensitive data using the dynamically generated encryption key, and store the encrypted data in the distributed storage network; S26. Record summary information, dynamic encryption metadata, and access permission rules for sensitive data on the blockchain network.

[0023] In this implementation, S3 specifically includes: S31, extracting access permission rules and encrypted metadata stored in the blockchain network, and parsing the data identifier in the access request submitted by the user; S32. Use the dynamic permission allocation mechanism inspired by the immune system to match user requests and permission rules according to the antigen-antibody matching principle: ; in, Represents user request u and permission rules The matching strength, represents the feature vector of user request u, Represents permission rules The eigenvector of , exp is the natural exponential function, is a hyperparameter that controls the width of the function; S33, verify whether the user request meets the access permission rules, record the matching result as an access verification log, and store it in the blockchain network; S34, extracting historical access logs from the blockchain network and analyzing the correlation between user behavior patterns and current access requests; S35. Based on the immune memory mechanism, dynamically adjust the priority and adaptability of access permission rules in combination with historical access behaviors: ; in, represents the weight of the access permission rule at time t, is the learning rate, is the dynamic correlation change of the access permission rule, is the weight of the access permission rule at time t+1, is the weight adjustment rate of the immune memory mechanism, It is the long-term memory value of the access permission rule; S36. Synchronously update the adjusted access permission rules and matching results to the blockchain network.

[0024] In this implementation, S4 specifically includes: S41, parsing the access target and permission conditions in the user request through the user access request and the access permission rule set; S42. Construct verification problems based on zero-knowledge proof technology to convert user requests into permission verification problems without leaking user sensitive information during the verification process; S43. Verify whether the user meets the access conditions by using zero-knowledge proof technology to complete the permission condition verification: ; in, is the verification result function, u is the access request submitted by the user, is a logical statement of a set of permission rules. Indicates the matching strength between the user request and the permission rule. is the permission rule, I is the indicator function, and m is the total number of permission rules; S44. When the verification is successful, the matching success status and the identifier of the corresponding permission rule are recorded; when the verification fails, the failure reason is recorded and a permission verification log is generated; S45. Store the permission condition verification result and the generated permission verification log in the blockchain network, including the user request summary and the matching rule identifier.

[0025] In this implementation manner, S5 specifically includes: S51, extracting permission logs and access records from the blockchain network, combining the permission rules and permission condition verification results, and generating a basic data set; S52. Construct a multi-objective reinforcement learning model based on the basic data set. The state of the model includes the permission state and access behavior, the action includes adjusting the audit strategy and task allocation, and the reward value reflects the optimization goals of audit efficiency, compliance and risk; S53. Use the multi-objective reinforcement learning model to analyze permission logs and access records, dynamically adjust the audit strategy based on the current status, and generate audit tasks: ; in, represents the optimal strategy under the current state, represents the current state, A represents the set of all optional actions in the model, a represents the action, Indicates in status The expected cumulative reward after taking action a, is the discount factor, Indicates the next state, Indicates that from the current state After taking action a, transfer to the next state The probability of Indicates the next state The reward value obtained when , argmax represents the action a used to find the maximum value in the brackets; S54. Prioritize audit tasks by reward value, and give priority to auditing data with higher risks; S55. Combine genetic algorithm to optimize the storage path and resource allocation scheme of audit tasks: ; ; in, is the comprehensive optimization objective function, x is the audit task execution plan, is the weight coefficient, Score the audit task priority, Score storage paths and execution efficiency, is the resource consumption cost, represents the next generation solution after genetic algorithm optimization, is a parent generation scheme of the current generation, Another parent generation scheme for the current generation, CrossOver() represents the crossover operation, and Mutate() represents the mutation operation; S56. Complete the audit task according to the optimized audit strategy, and store the audit results and optimization records in the blockchain network, including the audit result summary and resource allocation log.

[0026] In this implementation manner, S6 specifically includes: S61. For the generated audit tasks, the tasks are distributed to multiple nodes. Each node processes the local tasks independently and generates local verification results according to the local authority rules and verification logs. S62. Using privacy multi-party computing technology, each node shares the verification intermediate value through a security protocol without leaking the original data, completing the secure aggregation of local verification results: ; in, represents the global verification result, n represents the total number of nodes participating in the verification task, i represents the node index, is the node weight factor, g is the correction function, is the local verification result of node i, is the difference characteristic value of node i; S63. Based on the collaborative execution verification among nodes, synthesize the global verification results, which include cross-node data consistency and permission rule adaptability analysis; S64, recording the summary of the local verification result and the global verification result to the blockchain network; S65. Generate a report on the completion status of the audit task based on the global verification results recorded in the blockchain network, and associate the report with the verification records in the blockchain network.

[0027] In this implementation manner, the S7 specifically includes: S71. Combining global verification results, permission verification logs and permission rules, a multi-scale mathematical modeling framework is constructed, including cell-level mechanisms and group-level mechanisms. The cell-level mechanism is used to process a single request, and the group-level mechanism is used to optimize the global audit process. S72. In the cell-level mechanism, the permission verification rules of a single request are dynamically adjusted according to the permission verification rules and matching status of the single request: ; in, is the adjusted authority verification weight, is the authority verification weight, is the rule matching degree change, is the adjustment factor; S73. In the group-level mechanism, based on the global verification results and permission verification logs, analyze the global data access pattern and optimize the task priority and resource allocation strategy in the audit process: ; in, is the priority of task j, is the risk factor of the rule, is the resource consumption weight, and is the weight coefficient; S74. Combine the cell-level optimization results and the population-level optimization results of the multi-scale model to adjust the authority verification process and the global audit strategy; S75. Generate a compliance report based on the optimized permission verification process and audit strategy, including permission adjustment records, audit result summary and rule optimization plan, and store the report in the blockchain network.

[0028] Embodiment 1: In order to verify the feasibility of the present invention in implementation, the present invention is applied to an international medical data management platform, which needs to process patient information from multiple countries and regions, including personal identity information, medical records, and sensitive health data. Due to the complex regulatory requirements involved, the platform faces the dual pressures of data privacy protection and compliance management. Traditional systems use static permission allocation and rule verification, which cannot respond to permission adjustment needs in real time, resulting in an increased risk of data leakage and compliance verification delays. Especially in cross-border collaboration, due to significant differences in regulations in different countries, data sharing and compliance verification have become key bottlenecks.

[0029] In order to address the above problems, the blockchain-based data compliance and privacy protection method of the present invention is applied to the platform. By building a blockchain network and connecting global medical data nodes, distributed storage and transparent management of data are achieved. The present invention adopts a dynamic permission verification mechanism, adjusts permission verification rules in real time through a biologically inspired model, and dynamically allocates permissions based on user access requests, thereby improving the flexibility of access control. In data storage, sensitive data is dynamically encrypted through a pulse neural network, and the encrypted data is distributed and stored in global nodes. At the same time, the encryption metadata and permission rules are recorded on the blockchain to ensure the security of data during transmission and storage.

[0030] In addition, the present invention optimizes the compliance audit process through a multi-objective optimization algorithm. Using a reinforcement learning model, high-risk data is audited first, and combined with privacy multi-party computing technology, cross-regional compliance verification is completed without leaking original data. Finally, based on the transparent recording function of blockchain, a compliance report is generated to ensure the traceability of the entire verification process.

[0031] The test platform covers five major nodes, located in North America, Europe, Asia Pacific, Latin America and the Middle East, with a test period of 6 months. Evaluation indicators include permission management response time and cross-regional compliance verification time.

[0032] Table 1 Comparison of permission management response time

[0033] Table 2 Comparison of cross-regional compliance verification time

[0034] It can be seen from Table 1 that the dynamic permission allocation mechanism of the present invention significantly shortens the response time of permission management. In the North American node test, the traditional method response time is 220 milliseconds, while the present invention only takes 130 milliseconds, and the response efficiency is improved by 41%. Other nodes such as Europe, Asia-Pacific, Latin America and the Middle East all show similar improvements, with response efficiency improvements ranging from 36% to 42%. Overall, the average response time of the present invention is 120 milliseconds, which is 40% shorter than the 200 milliseconds of the traditional method. This shows that in a multi-node environment, the present invention can effectively improve the real-time performance of permission allocation, especially in complex scenarios with high-frequency permission adjustments, further enhancing the dynamic adaptability of the system.

[0035] Table 2 shows that the verification efficiency of the present invention in cross-regional collaboration has also been significantly improved. For example, between North American and European nodes, the traditional method requires 70 minutes to complete the verification, while the present invention only takes 18 minutes, a 74% reduction in time. The verification time between North American and Asia-Pacific, Latin American and Middle Eastern nodes has also been reduced from the traditional 65, 50 and 55 minutes to 15, 12 and 14 minutes, respectively, with an overall efficiency improvement of about 75%. On average, the traditional cross-regional verification time is 60 minutes, while the present invention only takes 15 minutes, an efficiency improvement of 75%. This shows that the present invention, which combines privacy multi-party computing technology and optimized audit strategy solutions, can significantly shorten the time for cross-regional verification, improve global collaboration efficiency, and fully protect data privacy during the verification process.

[0036] In summary, the present invention not only improves the response speed in authority management, but also optimizes the efficiency of compliance verification in cross-regional collaboration, greatly reducing time costs and resource consumption. This performance improvement has very important application value for multi-node distributed systems, especially those that require high-frequency authority adjustments and complex regulatory environments.

[0037] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data compliance and privacy protection method based on blockchain, characterized in that: The steps include: S1. Build a blockchain network, and the nodes upload the original data to the blockchain network as the data to be protected; S2. Classify the data to be protected, generate encryption keys using a pulse neural network, and record data summaries, encryption metadata, and access permission rules on the blockchain network; S3, combining access permission rules and encrypted metadata, using a dynamic allocation mechanism inspired by the immune system, using the antigen-antibody matching principle to verify user access requests, and dynamically adjusting permission rules through an immune memory mechanism; S4. Based on the completed permission verification results, use zero-knowledge proof technology to verify whether the user meets the access conditions and complete the permission condition verification; S5. Combine the results of the permission rules and permission conditions verification, use the multi-objective reinforcement learning model to adjust the audit strategy and generate audit tasks; determine the audit task priority through the reward function, and optimize the storage path and resource allocation in combination with the genetic algorithm; S6. For the generated audit tasks, we use privacy-preserving multi-party computing technology to process the audit tasks, perform verification collaboratively between nodes, and synthesize the global verification results. S7. Adjust the permission verification and audit process based on multi-scale mathematical modeling through global verification results, permission verification logs and permission rules, apply cell-level mechanisms to single request processing, and apply group-level mechanisms to the global audit process to generate compliance reports; S8. Combined with the compliance report, the complete permission verification and audit process is recorded on the blockchain network through the regulatory node.

2. According to a blockchain-based data compliance and privacy protection method according to claim 1, it is characterized in that: The S2 specifically includes: S21. Obtain the uploaded data to be protected, classify it based on data features, and distinguish between sensitive data and non-sensitive data; S22. Attach a privacy label to the sensitive data, where the privacy label includes a sensitivity level identifier and access permission rules; S23. Encode the data set marked as sensitive data and use the spiking neural network to generate discrete pulse signals for the sensitive data: ； in, is the membrane potential at time t, is the activation function, is the time decay function, is the time decay factor, i is the index of the neuron, is the neuron output signal of the spiking neural network; S24. Dynamically adjust synaptic weights based on the pulse neural network and generate encryption keys: ; in, is the dynamic encryption key generated at time t, H is the hash function, is the synaptic weight between the ith neuron and the jth neuron at time t, is the learning rate, Indicates time The pulse output signal of the jth neuron at time instant; S25. Encrypt the sensitive data using the dynamically generated encryption key, and store the encrypted data in the distributed storage network; S26. Record summary information, dynamic encryption metadata, and access permission rules for sensitive data on the blockchain network.

3. According to a blockchain-based data compliance and privacy protection method according to claim 1, it is characterized in that: The S3 specifically includes: S31, extracting access permission rules and encrypted metadata stored in the blockchain network, and parsing the data identifier in the access request submitted by the user; S32. Use the dynamic permission allocation mechanism inspired by the immune system to match user requests and permission rules according to the antigen-antibody matching principle: ； in, Represents user request u and permission rules The matching strength, represents the feature vector of user request u, Represents permission rules The eigenvector of , exp is the natural exponential function, is a hyperparameter that controls the width of the function; S33, verify whether the user request meets the access permission rules, record the matching result as an access verification log, and store it in the blockchain network; S34, extracting historical access logs from the blockchain network and analyzing the correlation between user behavior patterns and current access requests; S35. Based on the immune memory mechanism, dynamically adjust the priority and adaptability of access permission rules in combination with historical access behaviors: ； in, represents the weight of the access permission rule at time t, is the learning rate, is the dynamic correlation change of the access permission rule, is the weight of the access permission rule at time t+1, is the weight adjustment rate of the immune memory mechanism, It is the long-term memory value of the access permission rule; S36. Synchronously update the adjusted access permission rules and matching results to the blockchain network.

4. According to a blockchain-based data compliance and privacy protection method according to claim 1, it is characterized in that: The S4 specifically includes: S41, parsing the access target and permission conditions in the user request through the user access request and the access permission rule set; S42. Construct verification problems based on zero-knowledge proof technology to convert user requests into permission verification problems without leaking user sensitive information during the verification process; S43. Verify whether the user meets the access conditions by using zero-knowledge proof technology to complete the permission condition verification: ； in, is the verification result function, u is the access request submitted by the user, is a logical statement of a set of permission rules. Indicates the matching strength between the user request and the permission rule. is the permission rule, I is the indicator function, and m is the total number of permission rules; S44. When the verification is successful, the matching success status and the identifier of the corresponding permission rule are recorded; when the verification fails, the failure reason is recorded and a permission verification log is generated; S45. Store the permission condition verification result and the generated permission verification log in the blockchain network, including the user request summary and the matching rule identifier.

5. According to a blockchain-based data compliance and privacy protection method according to claim 1, it is characterized in that: The S5 specifically includes: S51, extracting permission logs and access records from the blockchain network, combining the permission rules and permission condition verification results, and generating a basic data set; S52. Construct a multi-objective reinforcement learning model based on the basic data set. The state of the model includes the permission state and access behavior, the action includes adjusting the audit strategy and task allocation, and the reward value reflects the optimization goals of audit efficiency, compliance and risk; S53. Use the multi-objective reinforcement learning model to analyze permission logs and access records, dynamically adjust the audit strategy based on the current status, and generate audit tasks: ； in, represents the optimal strategy under the current state, represents the current state, A represents the set of all optional actions in the model, a represents the action, Indicates in status The expected cumulative reward after taking action a, is the discount factor, Indicates the next state, Indicates that from the current state After taking action a, transfer to the next state The probability of Indicates the next state The reward value obtained when , argmax represents the action a used to find the maximum value in the brackets; S54. Prioritize audit tasks by reward value, and give priority to auditing data with higher risks; S55. Combine genetic algorithm to optimize the storage path and resource allocation scheme of audit tasks: ； ； in, is the comprehensive optimization objective function, x is the audit task execution plan, is the weight coefficient, Score the audit task priority, Score storage paths and execution efficiency, is the resource consumption cost, represents the next generation solution after genetic algorithm optimization, is a parent generation scheme of the current generation, Another parent generation scheme for the current generation, CrossOver() represents the crossover operation, and Mutate() represents the mutation operation; S56. Complete the audit task according to the optimized audit strategy, and store the audit results and optimization records in the blockchain network, including the audit result summary and resource allocation log.

6. According to a blockchain-based data compliance and privacy protection method according to claim 1, it is characterized in that: The S6 specifically includes: S61. For the generated audit tasks, the tasks are distributed to multiple nodes. Each node processes the local tasks independently and generates local verification results according to the local authority rules and verification logs. S62. Using privacy multi-party computing technology, each node shares the verification intermediate value through a security protocol without leaking the original data, completing the secure aggregation of local verification results: ； in, represents the global verification result, n represents the total number of nodes participating in the verification task, i represents the node index, is the node weight factor, g is the correction function, is the local verification result of node i, is the difference characteristic value of node i; S63. Based on the collaborative execution verification among nodes, synthesize the global verification results, which include cross-node data consistency and permission rule adaptability analysis; S64, recording the summary of the local verification result and the global verification result to the blockchain network; S65. Generate a report on the completion status of the audit task based on the global verification results recorded in the blockchain network, and associate the report with the verification records in the blockchain network.

7. According to a blockchain-based data compliance and privacy protection method according to claim 1, it is characterized in that: The S7 specifically includes: S71. Combining global verification results, permission verification logs and permission rules, a multi-scale mathematical modeling framework is constructed, including cell-level mechanisms and group-level mechanisms. The cell-level mechanism is used to process a single request, and the group-level mechanism is used to optimize the global audit process. S72. In the cell-level mechanism, the permission verification rules of a single request are dynamically adjusted according to the permission verification rules and matching status of the single request: ； in, is the adjusted authority verification weight, is the authority verification weight, is the rule matching degree change, is the adjustment factor; S73. In the group-level mechanism, based on the global verification results and permission verification logs, analyze the global data access pattern and optimize the task priority and resource allocation strategy in the audit process: ； in, is the priority of task j, is the risk factor of the rule, is the resource consumption weight, and is the weight coefficient; S74. Combine the cell-level optimization results and the population-level optimization results of the multi-scale model to adjust the authority verification process and the global audit strategy; S75. Generate a compliance report based on the optimized permission verification process and audit strategy, including permission adjustment records, audit result summary and rule optimization plan, and store the report in the blockchain network.

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