Block chain data sharing system based on privacy reinforcement learning

Abstract

The invention provides a blockchain data sharing system based on privacy reinforcement learning, and relates to the technical field of data sharing and privacy protection, and the system comprises a privacy reinforcement learning module which is used for carrying out reinforcement learning based on a data sharing strategy corresponding to historical data and a privacy risk assessment result, and generating a data sharing strategy of current data; the intelligent contract generation module is used for generating an intelligent contract corresponding to the current data based on the data sharing strategy of the current data; the block chain data sharing module is used for performing data sharing operation on the current data based on the smart contract corresponding to the current data; the privacy protection execution module is used for performing privacy protection on the current data based on the smart contract corresponding to the current data; and the real-time monitoring and feedback module is used for monitoring the execution process of the data sharing operation in real time and generating a privacy risk assessment result corresponding to the current data. According to the system provided by the invention, privacy protection of the data in the sharing process is ensured, and efficient utilization of the data is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing and privacy protection, and in particular to a blockchain data sharing system based on privacy reinforcement learning. Background Art

[0002] With the rapid development of big data and artificial intelligence, data has become an important resource to promote technological innovation and economic growth. However, the widespread sharing and use of data also brings serious challenges to privacy protection. Especially in scenarios that require multi-party collaboration, such as medical data sharing, financial risk control, smart city management, etc., data sharing needs to ensure the privacy of all parties' data and ensure the efficient use of data. Traditional data sharing methods usually rely on centralized storage and management models, which face risks such as single point failure, data leakage, and tampering, and are difficult to meet the needs of data privacy protection.

[0003] The emergence of blockchain technology provides a new approach to solving the privacy protection problem in data sharing. Through decentralized distributed ledger technology, blockchain achieves transparency, immutability and traceability of data storage and operation, greatly enhancing the security and credibility of data sharing. However, blockchain itself does not completely solve the problem of privacy protection. Although blockchain ensures the transparency of data operations, how to prevent the leakage of sensitive information in the process of data sharing is still a key issue.

[0004] Therefore, how to achieve privacy protection in the process of data sharing has become a technical problem that needs to be urgently solved in the industry. Summary of the invention

[0005] The present invention provides a blockchain data sharing system based on privacy reinforcement learning, which is used to solve the technical problem of how to achieve privacy protection in the process of data sharing.

[0006] The present invention provides a blockchain data sharing system based on privacy reinforcement learning, comprising: Privacy reinforcement learning module, smart contract generation module, blockchain data sharing module, privacy protection execution module and real-time monitoring and feedback module; The privacy reinforcement learning module is used to perform reinforcement learning based on the data sharing strategy corresponding to the historical data and the privacy risk assessment results to generate the data sharing strategy for the current data; The smart contract generation module is connected to the privacy reinforcement learning module and is used to generate a smart contract corresponding to the current data based on the data sharing strategy of the current data; the smart contract is used to define the data sharing rules and privacy protection algorithm corresponding to the current data; The blockchain data sharing module, which is connected to the smart contract generation module, is used to perform data sharing operations on the current data based on the smart contract corresponding to the current data; The privacy protection execution module, which is connected to the smart contract generation module and the blockchain data sharing module, is used to perform privacy protection on the current data based on the smart contract corresponding to the current data; The real-time monitoring and feedback module, which is connected to the blockchain data sharing module and the privacy reinforcement learning module, is used to perform real-time monitoring on the execution process of the data sharing operation and generate a privacy risk assessment result corresponding to the current data.

[0007] In some embodiments, the privacy reinforcement learning module is used for: Taking the execution result of the data sharing operation of the historical data as the state, taking the data sharing policy corresponding to the historical data as the action, and taking the privacy risk assessment result corresponding to the historical data as the reward, performing reinforcement learning to generate the data sharing policy of the current data.

[0008] In some embodiments, the blockchain data sharing module is used for: Receiving a data sharing request; Determining the data identifier corresponding to the data sharing request; Matching the data identifier with the data identifiers of each data in the blockchain to determine that the data identifier is the data identifier corresponding to the current data; Based on the smart contract corresponding to the current data, determining the data sharing rule corresponding to the current data; Based on the data sharing rule corresponding to the current data, performing data sharing operations on the current data; Generating an execution result of the data sharing operation of the current data.

[0009] In some embodiments, the blockchain data sharing module is used for: Based on the execution result of the data sharing operation of the current data and the data access record, generating an operation log corresponding to the current data; Recording the operation log corresponding to the current data in the distributed ledger of the blockchain.

[0010] In some embodiments, the privacy protection execution module is used for: Based on the smart contract corresponding to the current data, determining the privacy protection algorithm corresponding to the current data; Based on the privacy protection algorithm corresponding to the current data, performing privacy protection on the current data.

[0011] In some embodiments, the real-time monitoring and feedback module is configured to: Generate a privacy risk assessment result corresponding to the current data based on the execution result of the data sharing operation, the privacy protection assessment result, and the user feedback result of the current data.

[0012] In some embodiments, the data sharing rules include at least one of a data access control rule, a data usage period, and a usage restriction rule.

[0013] In some embodiments, the privacy protection algorithm includes at least one of differential privacy, homomorphic encryption, and multi-party secure computation.

[0014] In some embodiments, the system further includes: A data collection and preprocessing module, connected to the privacy reinforcement learning module, for collecting initial data from multiple data sources, preprocessing the initial data to obtain the current data; the preprocessing includes at least one of data denoising, format conversion, and encryption processing.

[0015] In some embodiments, the system further includes: A user interface and configuration module, connected to the privacy reinforcement learning module and the smart contract generation module, for displaying the data sharing rules, privacy protection algorithms, and execution results of data sharing operations of the current data, and dynamically adjusting the data sharing policy and / or smart contract corresponding to the current data in response to user input.

[0016] The blockchain data sharing system based on privacy reinforcement learning provided by the present invention has the following beneficial effects: (1) Protect data privacy: By combining privacy protection technologies with reinforcement learning, it is ensured that sensitive information will not be leaked or misused during the data sharing and usage process; by introducing privacy reinforcement learning technology, the system can optimize the data sharing policy while protecting data privacy, realizing secure and effective data sharing; (2) Realize intelligent optimization of data sharing policies: Automatically learn and optimize data sharing policies through reinforcement learning algorithms. The system can dynamically adjust data sharing rules according to real-time environmental changes and user needs, realizing personalized and intelligent data management; this can ensure that the data sharing process not only meets security requirements but also maximizes the utilization value of data; (3) Utilize blockchain technology to ensure the transparency and traceability of data sharing: Utilize the decentralization, immutability, and transparency of blockchain technology to ensure the legality and traceability of all data sharing operations; by recording the entire process of data sharing on the blockchain, a trustworthy collaboration platform can be provided for all participating parties, avoiding data abuse and disputes; (4) Support multi-party data collaboration and sharing: Provide a secure and efficient solution for multi-party data collaboration. Through the combination of blockchain and privacy-enhanced learning, the system can achieve secure data sharing among multiple participants while protecting the privacy interests of all parties and promoting cross-organizational and cross-domain data collaboration; (5) Provide a real-time monitoring and dynamic adjustment mechanism: Introduce a real-time monitoring and feedback mechanism to ensure that the system can continuously optimize and adjust the data sharing strategy according to the execution of data sharing and privacy risk assessment. Through dynamic adjustment, the system can maintain the best state in a rapidly changing environment to ensure the security and flexibility of data sharing. Brief Description of the Drawings

[0017] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments in line with the present invention and used together with the specification to explain the principles of the present invention.

[0018] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is one of the schematic structural diagrams of the blockchain data sharing system based on privacy-enhanced learning provided by the present invention.

[0020] Figure 2 It is the second schematic structural diagram of the blockchain data sharing system based on privacy-enhanced learning provided by the present invention. Detailed Embodiment

[0021] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units or modules does not necessarily have to be limited to those steps or units or modules clearly listed, but may include other steps or units or modules not clearly listed or inherent to these processes, methods, products or devices.

[0023] As an adaptive learning algorithm, reinforcement learning can optimize decision-making strategies in a dynamic environment. By introducing privacy protection mechanisms such as differential privacy and homomorphic encryption, reinforcement learning can automatically learn and optimize data sharing strategies while protecting data privacy to meet the requirements of different scenarios.

[0024] Currently, although blockchain technology and privacy protection technology have each made significant progress, in practical applications, how to combine blockchain with privacy-enhanced learning to build a system that can not only ensure data privacy but also achieve intelligent and dynamic data sharing still faces many technical challenges. For example, how to achieve dynamic adjustment of data sharing strategies through smart contracts under the decentralized architecture of blockchain; how to optimize the efficiency and flexibility of data sharing through reinforcement learning while protecting privacy; how to ensure that the system can adapt to environmental changes in real time and continuously optimize data sharing strategies, etc.

[0025] To address the deficiencies of related technologies, Figure 1 is one of the schematic structural diagrams of the blockchain data sharing system based on privacy-enhanced learning provided by the present invention. As Figure 1 shown, the blockchain data sharing system 100 includes a privacy-enhanced learning module 110, a smart contract generation module 120, a blockchain data sharing module 130, a privacy protection execution module 140, and a real-time monitoring and feedback module 150.

[0026] The privacy-enhanced learning module is used to perform reinforcement learning based on the data sharing strategy and privacy risk assessment result corresponding to historical data, and generate the data sharing strategy for the current data. The smart contract generation module is connected to the privacy-enhanced learning module and is used to generate a smart contract corresponding to the current data based on the data sharing strategy of the current data; the smart contract is used to define the data sharing rules and privacy protection algorithms corresponding to the current data. The blockchain data sharing module, connected to the smart contract generation module, is used to perform data sharing operations on the current data based on the smart contract corresponding to the current data; The privacy protection execution module, connected to the smart contract generation module and the blockchain data sharing module, is used to perform privacy protection on the current data based on the smart contract corresponding to the current data; The real-time monitoring and feedback module, connected to the blockchain data sharing module and the privacy reinforcement learning module, is used to perform real-time monitoring on the execution process of the data sharing operation and generate a privacy risk assessment result corresponding to the current data.

[0027] Specifically, the blockchain data sharing system based on privacy reinforcement learning provided by the embodiments of the present invention can be applied to scenarios that require multi-party collaboration, such as medical data sharing, financial risk control, and smart city management. The system can be built through a server cluster.

[0028] In terms of structure, the blockchain data sharing system can include a privacy reinforcement learning module, a smart contract generation module, a blockchain data sharing module, a privacy protection execution module, and a real-time monitoring and feedback module.

[0029] The privacy reinforcement learning module is the core module of the system and is responsible for automatically learning and optimizing data sharing strategies through reinforcement learning algorithms. The reinforcement learning algorithms can include Deep Q-Network (DQN), policy gradient, etc. During the learning process, the privacy reinforcement learning module combines technologies such as differential privacy and homomorphic encryption to ensure that data privacy is not leaked. This module can dynamically adjust the data sharing strategy according to system feedback to achieve personalized and intelligent data sharing.

[0030] The current data refers to the data that needs to be shared. The historical data refers to the data that has already been shared.

[0031] The data sharing strategy is the behavior and decision-making when guiding data sharing between different users or entities (such as individuals, organizations, enterprises, etc.). Its core goal is to ensure the security, compliance, and privacy protection of data while meeting the data usage requirements.

[0032] The privacy risk assessment result is the result of identifying, analyzing, and quantifying the possible privacy leakage risks in the data sharing process.

[0033] The privacy reinforcement learning module can obtain the privacy risk assessment result corresponding to the previous historical data from the real-time monitoring and feedback module, perform reinforcement learning based on the data sharing strategy and privacy risk assessment result corresponding to the historical data, and generate the data sharing strategy for the current data.

[0034] The smart contract generation module is connected to the privacy-enhanced learning module and is used to dynamically generate and deploy the smart contract corresponding to the current data according to the data sharing policy output by the privacy-enhanced learning module. The smart contract is used to define the data sharing rules and privacy protection algorithms corresponding to the current data. The automatic execution of the smart contract ensures the fairness and transparency of the data sharing process. The smart contract generation module can also adjust the content of the smart contract according to the real-time changing environment and requirements to maintain the dynamic adaptability of the data sharing policy.

[0035] Data sharing rules refer to a series of norms and guiding principles formulated to ensure the security, privacy protection, and compliance of data during the data sharing process, which may include data access control rules, data usage period, and usage restriction rules.

[0036] Privacy protection algorithms are a class of technologies and methods designed to protect data privacy. They ensure that sensitive information is not leaked during the processes of data collection, storage, processing, and sharing through various means. Privacy protection algorithms can include differential privacy, homomorphic encryption, and multi-party secure computation, etc.

[0037] Different privacy protection algorithms need to be adopted at different stages of data to address the privacy risks at each stage. For example, at the data generation stage, privacy can be protected by access restrictions and forged data; at the data storage stage, encryption technology and hybrid cloud storage can be adopted; at the data processing stage, technologies such as differential privacy, secure multi-party computation, and federated learning can be used. The selection and combined application of these algorithms can effectively protect data privacy while meeting the needs of data utilization.

[0038] The blockchain data sharing module, also known as the blockchain data sharing layer, is the infrastructure for data sharing and is responsible for storing and managing all data sharing operations, the execution status of smart contracts, and data access history. The blockchain data sharing module can obtain the smart contract corresponding to the current data from the smart contract generation module and perform data sharing operations on the current data according to the data sharing rules in the smart contract. The decentralized feature of the blockchain ensures the transparency and immutability of data operations. All data sharing activities are recorded in the distributed ledger, guaranteeing the trust and traceability of all parties in data usage. The blockchain can be selected as a public chain, private chain, consortium chain, etc. The specific type of blockchain is not specifically limited in the embodiments of the present invention.

[0039] The privacy protection execution module is connected to the smart contract generation module and is used to perform privacy protection on the current data according to the type and usage rules of the privacy protection algorithm in the smart contract. This module integrates a variety of privacy protection technologies, such as differential privacy, homomorphic encryption, and Secure Multi-Party Computation (MPC), to ensure that sensitive data will not be leaked or misused during the data sharing and reinforcement learning processes. The privacy protection mechanism is applied in all aspects of data transmission, processing, and storage to ensure the privacy security of the entire system.

[0040] The real-time monitoring and feedback module, which is connected to the blockchain data sharing module and the privacy reinforcement learning module, is used to monitor the execution process of data sharing operations in real time, collect and analyze data usage, privacy protection effects, and user feedback, and generate a privacy risk assessment result corresponding to the current data. The privacy risk assessment result refers to a quantitative or qualitative description of the possible risks that data processing activities may pose to privacy.

[0041] The privacy risk assessment result can be sent to the privacy reinforcement learning module as a basis for adjusting the data sharing strategy. This feedback mechanism ensures that the system can quickly adapt to environmental changes, continuously optimize the data sharing strategy, and maintain the efficient operation of the system.

[0042] The blockchain data sharing system based on privacy reinforcement learning provided by the embodiments of the present invention has the following beneficial effects: (1) Protect data privacy: By combining privacy protection technologies with reinforcement learning, it is ensured that sensitive information will not be leaked or misused during the data sharing and usage processes; by introducing privacy reinforcement learning technology, the system can optimize the data sharing strategy while protecting data privacy, achieving secure and effective data sharing; (2) Realize intelligent optimization of data sharing strategy: Automatically learn and optimize the data sharing strategy through reinforcement learning algorithms. The system can dynamically adjust the data sharing rules according to real-time environmental changes and user needs, realizing personalized and intelligent data management; this can ensure that the data sharing process not only meets security requirements but also maximizes the utilization value of data; (3) Utilize blockchain technology to ensure the transparency and traceability of data sharing: Utilize the decentralization, immutability, and transparency of blockchain technology to ensure the legality and traceability of all data sharing operations; by recording the entire process of data sharing on the blockchain, a trustworthy collaboration platform can be provided for all participating parties, avoiding data abuse and disputes; (4)Support multi-party data collaboration and sharing: Provide a secure and efficient solution for multi-party data collaboration. Through the combination of blockchain and privacy-enhanced learning, the system can achieve secure data sharing among multiple participants while protecting the privacy interests of all parties and promoting cross-organizational and cross-domain data collaboration; (5)Provide real-time monitoring and dynamic adjustment mechanism: Introduce real-time monitoring and feedback mechanisms to ensure that the system can continuously optimize and adjust data sharing policies based on the execution of data sharing and privacy risk assessment. Through dynamic adjustment, the system can maintain the best state in a rapidly changing environment to ensure the security and flexibility of data sharing.

[0043] In some embodiments, the privacy-enhanced learning module is used for: Taking the execution results of data sharing operations of historical data as states, taking the data sharing policies corresponding to historical data as actions, and taking the privacy risk assessment results corresponding to historical data as rewards, perform reinforcement learning to generate the data sharing policy for the current data.

[0044] Specifically, the privacy-enhanced learning module may include a reinforcement learning algorithm unit and a privacy protection integration unit.

[0045] The reinforcement learning algorithm unit generates the data sharing policy in the following ways: State definition: Taking the execution results of data sharing operations of historical data as states. These states reflect the effects and privacy risk situations of past data sharing.

[0046] Action definition: Taking the data sharing policies corresponding to historical data as actions. These policies are the objects that the module needs to learn and optimize.

[0047] Reward definition: Taking the privacy risk assessment results corresponding to historical data as rewards. The privacy risk assessment results quantify the privacy risks of data sharing operations, and lower risk values usually correspond to higher rewards.

[0048] Reinforcement learning process: Based on the above states, actions, and rewards, the module uses reinforcement learning algorithms (such as DQN in deep reinforcement learning, etc.) for training. Through continuous trial and error, the algorithm learns to select the optimal action (i.e., the data sharing policy) in different states to maximize the cumulative reward (i.e., minimize the privacy risk).

[0049] The privacy protection integration unit is used to apply privacy protection technologies such as differential privacy, homomorphic encryption, and multi-party secure computing during the reinforcement learning process to ensure the privacy of data during the learning and reasoning processes.

[0050] The blockchain data sharing system based on privacy-enhanced learning provided by the embodiments of the present invention, through privacy-enhanced learning, continuously collects the privacy risk assessment results corresponding to historical data during the reinforcement learning process, adjusts the policy parameters, and finally forms an optimal data sharing policy.

[0051] In some embodiments, the blockchain data sharing module is used for: Receiving a data sharing request; Determining the data identifier corresponding to the data sharing request; Matching the data identifier with the data identifiers of each data in the blockchain to determine that the data identifier is the data identifier corresponding to the current data; Based on the smart contract corresponding to the current data, determining the data sharing rule corresponding to the current data; Performing a data sharing operation on the current data based on the data sharing rule corresponding to the current data; Generating the execution result of the data sharing operation of the current data.

[0052] Specifically, the blockchain data sharing module includes a smart contract management unit. This unit is used to receive a data sharing request from a data requester. This request usually includes the identity information of the requester, the description of the required data, and the purpose of the request.

[0053] Parsing the data sharing request, extracting the unique identifier (such as a data identifier or a hash value) of the required data to determine the specific data of the request. Then, matching the data identifier in the request with the data identifiers of each data stored on the blockchain to confirm whether the requested data exists. If the data identifier matches the data identifier corresponding to the current data, it can be determined that the requested data is the current data.

[0054] Once it is confirmed that the current data exists, the blockchain data sharing module obtains the data sharing rule corresponding to the data through the smart contract, and then performs a data sharing operation on the current data. During the operation, the execution result of the data sharing operation is generated and recorded, including the operation time, requester information, hash value of the shared data, etc.

[0055] In the blockchain data sharing system based on privacy-enhanced learning provided by the embodiments of the present invention, the optimized data sharing policy is deployed on the blockchain through a smart contract; by parsing the data sharing request through the blockchain data sharing module, each data sharing request triggers the smart contract to execute the corresponding rules, and only the operations that meet the permission requirements can be executed.

[0056] In some embodiments, the blockchain data sharing module is used for: Generating an operation log corresponding to the current data based on the execution result of the data sharing operation of the current data and the data access record; Record the operation log corresponding to the current data in the distributed ledger of the blockchain.

[0057] Specifically, the blockchain data sharing module further includes a distributed ledger storage unit. After completing the data sharing operation of the current data, this unit generates a detailed operation log based on the execution result of the operation and the data access record. The operation log records the key information in the data sharing process, including the operation time, the operation subject, the operation content, the data identifier, the sharing result, etc.

[0058] Record the operation log corresponding to the current data in the distributed ledger of the blockchain to ensure the immutability and permanent storage of the log. Through blockchain technology, the operation log can be synchronously stored on multiple nodes, enhancing the security and credibility of the data. Any query or audit of the data sharing operation can be traced through the blockchain ledger.

[0059] In the blockchain data sharing system based on privacy-enhanced learning provided by the embodiments of the present invention, the blockchain, as a distributed ledger, records all data sharing operations and rules to ensure the transparency and immutability of data operations; the blockchain also records the logs of all operations, providing a basis for subsequent audits and tracking.

[0060] In some embodiments, the privacy protection execution module is used for: Determine the privacy protection algorithm corresponding to the current data based on the smart contract corresponding to the current data; Perform privacy protection on the current data based on the privacy protection algorithm corresponding to the current data.

[0061] Specifically, in the data sharing process, the privacy protection execution module is responsible for executing privacy protection algorithms such as differential privacy, homomorphic encryption, and multi-party secure computation to ensure that the data is always protected during the sharing process. Even during data transmission and use, the system can prevent the leakage of sensitive information.

[0062] The privacy protection execution module may specifically include a differential privacy protection unit, a homomorphic encryption processing unit, and a multi-party secure computation unit.

[0063] In the blockchain data sharing system based on privacy-enhanced learning provided by the embodiments of the present invention, the privacy protection intensity and method are dynamically adjusted through privacy protection algorithms to ensure the optimal privacy protection effect in different usage scenarios.

[0064] In some embodiments, the smart contract generation module dynamically creates and updates smart contracts according to the policies output by the privacy-enhanced learning module. These contracts define the rules of data sharing, including access rights, usage time, data usage, etc., and are executed through the blockchain.

[0065] Each data sharing operation is constrained by the corresponding smart contract, ensuring that data can only be accessed and used according to predetermined rules, and avoiding any unauthorized operations.

[0066] In some embodiments, the real-time monitoring and feedback module is used for: Generating a privacy risk assessment result corresponding to the current data based on the execution result of the data sharing operation, the privacy protection evaluation result, and the user feedback result of the current data.

[0067] Specifically, the real-time monitoring and feedback module includes a data sharing monitoring unit and a feedback processing unit. The former can perform real-time monitoring on the execution process of the data sharing operation, and collect the execution result of the data sharing operation in real time, including information such as whether the operation is successful, the operation time, the operation subject, and the data identifier. These information reflect the actual execution situation of the data sharing process and are important bases for evaluating privacy risks; collect the privacy protection evaluation results generated by the privacy protection execution module, including the execution situation of the privacy protection algorithm, the usage situation of the privacy budget, etc. These information reflect the effectiveness of the privacy protection measures and are important references for evaluating privacy risks; collect user feedback on the data sharing operation, including the user's satisfaction with privacy protection, the privacy problems found, etc. User feedback is an important supplement for evaluating privacy risks and can reflect problems in actual use.

[0068] Finally, a privacy risk assessment result corresponding to the current data is generated based on the execution result of the data sharing operation, the privacy protection evaluation result, and the user feedback result. The feedback processing unit sends the privacy risk assessment result to the privacy reinforcement learning module so that the system can optimize the data sharing strategy according to the real-time feedback information.

[0069] The blockchain data sharing system based on privacy reinforcement learning provided by the embodiments of the present invention monitors the data sharing operation, the privacy protection effect, and the user feedback on the blockchain through the real-time monitoring and feedback module. The system will return the monitoring results to the privacy reinforcement learning module as a basis for further optimizing the data sharing strategy; the feedback information collected during the monitoring process is used to adjust the privacy protection mechanism and the data sharing strategy to ensure that the system can adapt to environmental changes and continuously maintain an efficient working state.

[0070] In some embodiments, Figure 2 is the second structural schematic diagram of the blockchain data sharing system based on privacy reinforcement learning provided by the present invention, as Figure 2 shown, the system further includes a data acquisition and preprocessing module 160 and a user interface and configuration module 170.

[0071] The data acquisition and preprocessing module, which is connected to the privacy-enhanced learning module, is used to collect initial data from multiple data sources, preprocess the initial data to obtain the current data; the preprocessing includes at least one of data denoising, format conversion, and encryption processing.

[0072] The user interface and configuration module, which is connected to the privacy-enhanced learning module and the smart contract generation module, is used to display the data sharing rules, privacy protection algorithms, and execution results of data sharing operations of the current data, and to dynamically adjust the data sharing policies and / or smart contracts corresponding to the current data in response to user input.

[0073] Specifically, the data acquisition and preprocessing module first collects the data to be shared from multiple data sources. These data usually contain sensitive information. Therefore, during the acquisition process, the system will perform preliminary preprocessing, including data denoising, format conversion, and basic encryption processing, to ensure the security of the data during transmission and storage. The preprocessed data is transmitted to the privacy-enhanced learning module of the system through a secure channel to prepare for subsequent privacy-enhanced learning and blockchain storage.

[0074] The user interface and configuration module may include a user operation interface unit and a user configuration management unit. The user operation interface unit can be used to display the data sharing rules, privacy protection algorithms, and execution results of data sharing operations of the current data in the interaction interface. The user configuration management unit is used to customize and manage the data sharing policies and smart contracts according to the actual needs in response to user input for dynamic adjustment, so as to achieve personalized data sharing management.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0076] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A blockchain data sharing system based on privacy-enhanced learning, characterized in that, Including: A privacy-enhanced learning module, a smart contract generation module, a blockchain data sharing module, a privacy protection execution module, and a real-time monitoring and feedback module; The privacy-enhanced learning module is used to perform reinforcement learning based on the data sharing policy and privacy risk assessment result corresponding to the historical data, and generate a data sharing policy for the current data; The smart contract generation module is connected to the privacy-enhanced learning module and is used to generate a smart contract corresponding to the current data based on the data sharing policy of the current data; the smart contract is used to define the data sharing rules and privacy protection algorithms corresponding to the current data; The blockchain data sharing module is connected to the smart contract generation module and is used to perform data sharing operations on the current data based on the smart contract corresponding to the current data; The privacy protection execution module is connected to the smart contract generation module and the blockchain data sharing module, and is used to perform privacy protection on the current data based on the smart contract corresponding to the current data; The real-time monitoring and feedback module is connected to the blockchain data sharing module and the privacy-enhanced learning module, and is used to perform real-time monitoring on the execution process of the data sharing operation, and generate a privacy risk assessment result corresponding to the current data.

2. The blockchain data sharing system based on privacy-enhanced learning according to claim 1, wherein The privacy-enhanced learning module is used for: Taking the execution result of the data sharing operation of the historical data as the state, taking the data sharing policy corresponding to the historical data as the action, and taking the privacy risk assessment result corresponding to the historical data as the reward, performing reinforcement learning, and generating a data sharing policy for the current data.

3. The blockchain data sharing system based on privacy-enhanced learning according to claim 1, characterized in that, The blockchain data sharing module is used for: Receiving a data sharing request; Determining the data identifier corresponding to the data sharing request; Matching the data identifier with the data identifiers of each data in the blockchain to determine that the data identifier is the data identifier corresponding to the current data; Based on the smart contract corresponding to the current data, determining the data sharing rules corresponding to the current data; Based on the data sharing rules corresponding to the current data, performing data sharing operations on the current data; Generating an execution result of the data sharing operation of the current data.

4. The blockchain data sharing system based on privacy-enhanced learning according to claim 3, characterized in that, The blockchain data sharing module is used for: Generating an operation log corresponding to the current data based on the execution result of the data sharing operation of the current data and the data access record; Recording the operation log corresponding to the current data in the distributed ledger of the blockchain.

5. The blockchain data sharing system based on privacy-enhanced learning according to claim 1, characterized in that The privacy protection execution module is used for: Based on the smart contract corresponding to the current data, determining the privacy protection algorithm corresponding to the current data; Based on the privacy protection algorithm corresponding to the current data, performing privacy protection on the current data.

6. The blockchain data sharing system based on privacy-enhanced learning according to claim 1, wherein The real-time monitoring and feedback module is used for: Generating a privacy risk assessment result corresponding to the current data based on the execution result of the data sharing operation of the current data, the privacy protection assessment result, and the user feedback result.

7. The blockchain data sharing system based on privacy-enhanced learning according to claim 1, wherein The data sharing rules include at least one of data access control rules, data usage period, and usage restriction rules.

8. The blockchain data sharing system based on privacy-enhanced learning according to claim 1, wherein The privacy protection algorithms include at least one of differential privacy, homomorphic encryption, and multi-party secure computation.

9. The blockchain data sharing system based on privacy-enhanced learning according to any one of claims 1 to 8, characterized in that, The system further includes: A data acquisition and preprocessing module, connected to the privacy-enhanced learning module, for collecting initial data from multiple data sources, preprocessing the initial data to obtain the current data; the preprocessing includes at least one of data denoising, format conversion, and encryption processing.

10. The blockchain data sharing system based on privacy-enhanced learning according to any one of claims 1 to 8, characterized in that, The system further includes: A user interface and configuration module, connected to the privacy-enhanced learning module and the smart contract generation module, for displaying the data sharing rules, privacy protection algorithms, and execution results of data sharing operations of the current data, and dynamically adjusting the data sharing policies and / or smart contracts corresponding to the current data in response to user input.

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