A method for managing personal data sharing based on big data

Abstract

The present invention discloses a method for managing personal data sharing based on big data, which relates to the technical field of data management and includes a blockchain management module, a smart contract management module, a data sharing negotiation module, a data traceability module, a data dynamic management module, and an anonymization processing module. This method for managing personal data sharing based on big data ensures the immutability and transparency of personal data through encryption and blockchain-based management, avoiding the risk of data leakage in traditional storage methods. The dynamic management mechanism of smart contracts and data can dynamically adjust the rules of data sharing, enhancing the flexibility and compliance in the data sharing process. Multi-party computing and compliance evaluation ensure privacy protection and rationality in data sharing, avoiding conflicts or abuse behaviors among multiple participants during the sharing process. It provides an efficient, secure, compliant, and transparent solution for personal data sharing while protecting the privacy rights and interests of users.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a method for managing personal data sharing based on big data. Background Art

[0002] With the rapid development of big data and artificial intelligence technologies, the value of personal data has been widely recognized. Many industries rely on data analysis and sharing to drive innovation and optimize business operations. However, the issues of data privacy and security are becoming increasingly serious. Especially in the scenario of cross-platform and cross-domain data sharing, how to ensure that personal data (such as personal medical and health data, etc.) can meet business needs while effectively protecting user privacy during the sharing process has become a major challenge faced by current technologies.

[0003] Existing personal data sharing technologies usually rely on traditional centralized storage and access models, lacking effective data privacy protection and compliance management mechanisms. For example, the permission management in data sharing is usually static and difficult to adapt to complex and dynamic sharing requirements. At the same time, when sharing data among multiple parties, it is difficult to achieve data privacy protection and responsibility division for all parties, which easily leads to data leakage or abuse. Also, there are deficiencies in the compliance assessment of data sharing and the automated adjustment of permissions, unable to meet the rapidly changing sharing needs. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for managing personal data sharing based on big data to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In the first aspect, an embodiment of the present invention provides a method for managing personal data sharing based on big data, including:

[0007] Encrypting personal data and then managing it on the blockchain;

[0008] Automatically configuring the sharing permissions of personal data through smart contracts in the blockchain;

[0009] After the smart contract configures the permissions, conducting data sharing negotiation to reach a compliant sharing agreement;

[0010] Based on the sharing agreement, conducting decentralized data tracking and tracing;

[0011] Conducting dynamic management of data, and the system automatically adjusts the sharing rules.

[0012] To further optimize this technical solution, in the blockchain management after encrypting personal data, it includes:

[0013] First, the encryption algorithm AES-256 is used to encrypt personal data, and blockchain technology is used to manage the encrypted personal data;

[0014] The access control of personal data and the management of log records are carried out through smart contracts. The ownership, usage rights, and authorization information of personal data are all recorded in real time. Any access, update, or deletion of personal data is recorded in the blockchain, forming a transparent and immutable access history.

[0015] To further optimize this technical solution, the smart contract automatically configures the sharing permissions of personal data, and the automatic configuration process includes:

[0016] The user sets the access permissions for their own personal data. The access permissions include restricting specific parties from accessing personal data under what circumstances and for what purposes, as well as setting conditional restrictions based on the time and frequency of data access;

[0017] The smart contract automatically checks the permissions during data access to ensure that only authorized specific parties can access the user's personal data;

[0018] And automatically adjusts the permission configuration according to the user's behavior. If the user has not viewed the usage of data for a long time, the smart contract automatically triggers a process to re-examine the permission settings.

[0019] To further optimize this technical solution, the sharing negotiation of the data includes:

[0020] Among multiple data providers, joint calculations are carried out in an encrypted manner. Each party can only see its own data. Before data sharing, all participating parties negotiate and confirm the conditions for sharing data through a smart contract, including the purpose of use, time range, and responsibility division.

[0021] To further optimize this technical solution, when conducting joint calculations, a multi-party calculation negotiation model is constructed to measure the data contribution of each participating party and the rationality of sharing data:

[0022] It is assumed that there are parties participating in the data sharing negotiation in the sharing scenario, and it is assumed that the private data provided by each party is . Joint calculations are carried out and modeled. It is assumed that the private data of each party is a vector, and the dimension of each data vector is , that is, ; ;

[0023] Each party conducts joint calculations through multi-party calculations. At the same time, during the calculation process, no party can directly know the data of other parties.

[0024] To further optimize this technical solution, the multi-party computing negotiation model includes a data contribution formula and a compliance evaluation formula;

[0025] The data contribution formula is used to evaluate the contribution and privacy protection of each participant to the shared data;

[0026] The compliance evaluation formula is used to quantify the compliance of the shared data of all parties.

[0027] To further optimize this technical solution, the data contribution formula is as follows:

[0028] ;

[0029] Wherein,

[0030] represents the data contribution degree of the th party;

[0031] is the weight of the data dimension for the sharing purpose, indicating the importance of the data in this dimension;

[0032] is the privacy protection function for the data of the th party in the th dimension, using differential privacy function, data encryption function;

[0033] The compliance evaluation formula is as follows:

[0034] ;

[0035] Wherein,

[0036] is the compliance score of the sharing protocol, reflecting whether the multi-party negotiation meets the compliance standards;

[0037] is the weight of each party in the compliance protocol, indicating the influence of each party in the final negotiation;

[0038] is the data contribution degree of the th party;

[0039] is the commitment of the th party to the compliance of the shared data, indicating whether this party strictly follows the compliance protocol.

[0040] To further optimize this technical solution, when performing dynamic management of data, by analyzing user behavior, data usage, and information on relevant laws and regulations in real time, the system automatically adjusts sharing rules according to different situations;

[0041] When the usage frequency of a certain data sharing party changes, the system automatically analyzes and prompts whether relevant permissions need to be adjusted, and temporarily freezes data sharing under specific circumstances.

[0042] To further optimize this technical solution, during the data sharing process, the system also includes de-identifying and anonymizing using multi-dimensional data;

[0043] Using methods such as k-anonymity and differential privacy to de-identify users' personal information so that the data cannot be directly associated with personal identities;

[0044] Establish an anonymization protocol to hide users' personal identity information.

[0045] To further optimize this technical solution, this technical solution includes the following functional modules:

[0046] A blockchain management module for encrypting personal data and recording the encrypted data on the blockchain;

[0047] An intelligent contract management module for managing the permissions, rules, and conditions of personal data sharing and performing automated management using intelligent contracts;

[0048] A data sharing negotiation module for protecting privacy through multi-party computing and evaluating the compliance of data sharing at the same time;

[0049] A data tracking and tracing module for real-time tracking and tracing of the flow and usage history of data during the sharing process;

[0050] A data dynamic management module for dynamically managing the sharing rules of shared data according to actual situations;

[0051] An anonymization processing module for de-identifying and anonymizing users' personal information.

[0052] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program instructions are executed by the processor, the steps of a method for managing personal data sharing based on big data as described in the first aspect of the present invention are implemented.

[0053] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program instructions are executed by the processor, the steps of a method for managing personal data sharing based on big data as described in the first aspect of the present invention are implemented.

[0054] Compared with the prior art, the present invention provides a method for managing personal data sharing based on big data, which has the following beneficial effects:

[0055] The method for managing personal data sharing based on big data ensures the immutability and transparency of personal data through encryption and blockchain management, avoiding the risk of data leakage in traditional storage methods. The intelligent contract and the dynamic management mechanism of data can dynamically adjust the rules of data sharing, improving the flexibility and compliance in the data sharing process. Multi-party computing and compliance evaluation ensure privacy protection and rationality in data sharing, avoiding conflicts or abuse behaviors among multiple participants during the sharing process. It provides an efficient, secure, compliant, and transparent solution for personal data sharing, promoting the development of the data economy while protecting the privacy rights and interests of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only 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.

[0057] Figure 1 It is a schematic diagram of the usage process of a method for managing personal data sharing based on big data proposed by the present invention;

[0058] Figure 2 It is a schematic diagram of the structure of a method for managing personal data sharing based on big data proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0060] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0061] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0062] Example 1:

[0063] Referring to Figure 1 , this is the first embodiment of the present invention. This embodiment provides a method for managing personal data sharing based on big data. The system includes:

[0064] Personal data is encrypted and then managed by blockchain

[0065] In this embodiment, in big data sharing, protecting personal privacy and information security is the most basic requirement. First, the encryption algorithm AES-256 is used to encrypt personal data, so that even if the data is attacked externally during transmission or storage, it cannot be cracked. To further enhance the immutability and transparency of the data, blockchain technology is used to manage the encrypted personal data.

[0066] The access rights control and log record management of personal data are carried out through smart contracts. The ownership, usage rights, and authorization information of personal data are all recorded in real time. Any access, update, or deletion of personal data is recorded in the blockchain, forming a transparent and immutable access history.

[0067] Automatically configure the sharing rights of personal data through smart contracts in the blockchain

[0068] In this embodiment, the user sets the access rights of their personal data. The access rights include restricting which specific parties can access personal data under what circumstances and for what purposes, as well as setting conditions based on the time and frequency of data access.

[0069] The smart contract automatically checks the rights during data access to ensure that only authorized specific parties can access the user's personal data.

[0070] And automatically adjusts the rights configuration according to the user's behavior. If the user has not viewed the usage of the data for a long time, the smart contract automatically triggers the process of reexamining the rights settings.

[0071] After the smart contract configures the rights, it conducts data sharing negotiation to reach a compliant sharing agreement

[0072] In this embodiment, among multiple data providers, joint calculation is carried out through encryption. Each party can only see its own data and cannot directly access the data of other parties, avoiding information leakage. Before data sharing, all participating parties negotiate and confirm the conditions for sharing data through smart contracts, including the purpose of use, time range, and responsibility division. In this way, different parties can share data safely while ensuring that the data is not leaked or misused during the sharing process. Calculate the rationality of each party's shared data to determine whether a compliant sharing agreement is reached.

[0073] Furthermore, when performing the joint calculation, a multi-party calculation negotiation model is constructed to measure the data contribution of each participant and the rationality of the shared data:

[0074] Set that there are parties participating in the data sharing negotiation, and set that each party provides private data as , perform joint calculation and modeling, and set the private data of each party is a vector, and the dimension of each data vector is , that is ;

[0075] Each party performs joint calculation through multi-party calculation. At the same time, during the calculation process, no party can directly know the data of other parties.

[0076] Through the encrypted joint calculation and smart contract negotiation mechanism, ensure that when participating parties share data:

[0077] Data privacy protection: Each party can only see its own data and cannot directly access the data of other parties.

[0078] Compliance and liability division: All parties can negotiate and confirm conditions such as the purpose of use and time range of the shared data to ensure compliance.

[0079] Data rationality calculation: Based on specific algorithms and formulas, calculate the rationality of the shared data of each party to ensure that there will be no data conflicts or abuses.

[0080] The multi-party calculation negotiation model includes a data contribution degree formula and a compliance evaluation formula;

[0081] The data contribution degree formula is used to evaluate the contribution of each participant to the shared data and the privacy protection situation;

[0082] The compliance evaluation formula is used to quantify the compliance of the shared data of all parties.

[0083] The data contribution degree formula is as follows:

[0084] ;

[0085] Among them,

[0086] represents the data contribution degree of the th party;

[0087] is the data dimension weight for the sharing purpose, represents the importance of the data in this dimension;

[0088] is a privacy protection function for the party's data in the dimension, using differential privacy function and data encryption function.

[0089] The compliance evaluation formula is as follows:

[0090] ;

[0091] where

[0092] is the compliance score of the sharing protocol, reflecting whether the multi-party negotiation meets the compliance standards;

[0093] is the weight of each party in the compliance protocol, indicating the influence of each party in the final negotiation;

[0094] is the party's data contribution;

[0095] is the party's commitment to the compliance of the shared data, indicating whether the party strictly follows the compliance protocol.

[0096] When this model is used, it includes:

[0097] Smart contract negotiation before data sharing: Before data sharing, all participating parties will define their respective data sharing rules, privacy protection requirements, and compliance commitments through smart contracts.

[0098] Each party's data privacy protection and compliance commitment are defined through functions and . Each party sets and according to the data sharing conditions provided by the smart contract.

[0099] Calculation and encryption process: The data provider encrypts its own provided data vector and performs joint calculations. Each party can only obtain the encrypted result of its own data and will not directly access the private data of other parties.

[0100] During this process, the data contribution of each party is calculated .

[0101] Compliance evaluation and sharing confirmation: After all data calculations and compliance evaluations are completed, the system will evaluate the contribution and compliance of all participating parties Perform a weighted sum to obtain a compliance score .

[0102] If the compliance score , the system confirms the establishment of the data sharing agreement, and all participating parties can securely share data under the supervision of the smart contract.

[0103] If the compliance score is lower than the threshold , the smart contract will prompt that the data sharing conditions or data privacy protection requirements need to be adjusted.

[0104] Ultimately, the sharing behaviors of all parties will be restricted by the compliance assessment, and the compliance score must be greater than a certain threshold to achieve a compliant data sharing agreement.

[0105] Based on the sharing agreement, perform decentralized data tracking and tracing

[0106] In this embodiment, in data sharing management, how to track the flow and usage history of data is the key to ensuring data security and compliance. Introduce a decentralized data tracking mechanism and use the distributed ledger of the blockchain to record every data flow and usage. Each time data is accessed or transmitted, a log will be automatically generated through the smart contract and recorded on the blockchain to ensure the integrity and accuracy of the data. Each user and third party can query the data usage records within their own permissions and can raise objections or demand accountability for non-compliant access behaviors. The decentralized tracking method ensures the transparency of data flow, enabling all participating parties to have a clear understanding of the data usage situation and making it impossible to be tampered with.

[0107] Perform dynamic management of data, and the system automatically adjusts the sharing rules

[0108] In this embodiment, by analyzing user behaviors, data usage, and information on relevant laws and regulations in real time, the system automatically adjusts the sharing rules according to different situations.

[0109] When the usage frequency of a certain data sharing party changes, the system automatically analyzes and prompts whether the relevant permissions need to be adjusted, and temporarily freezes data sharing in specific situations.

[0110] It is also possible to perform real-time auditing, record and analyze all data accesses, promptly detect potential non-compliant behaviors, and be able to issue warnings or automatically block through the smart contract.

[0111] During the data sharing process, the system also includes using multi-dimensional data for de-identification and anonymization processing;

[0112] Adopt k-anonymity and differential privacy data desensitization technology to de-identify users' personal information, making the data unable to be directly associated with personal identities;

[0113] Establish an anonymization protocol to ensure that even if the data is shared with a third party, the personal identity information of the user cannot be restored.

[0114] The de-identification process not only meets the requirements of privacy protection but also ensures that user privacy is not leaked during the big data analysis process. The anonymized data after data processing can still provide valuable information during analysis, modeling, statistics, etc., while minimizing the risk of data leakage.

[0115] Example 2:

[0116] Refer to Figure 2 , which is the second embodiment of the present invention. This embodiment provides the module composition of the personal data sharing management method based on big data.

[0117] The system includes the following functional modules:

[0118] The blockchain management module is used to encrypt personal data and record the encrypted data on the blockchain. Manage the access rights and access logs of the data through smart contracts.

[0119] The smart contract management module is used to manage the permissions, rules, and conditions for personal data sharing, and perform automated management using smart contracts. Set different sharing permissions for personal data, such as sharing purposes, access parties, time ranges, etc. Use smart contracts to automatically perform permission checks and adjustments according to the conditions set by users. The system will evaluate in real time whether the rules meet the current data sharing requirements and automatically adjust the permissions to ensure compliance.

[0120] The data sharing negotiation module is used to protect privacy through multi-party computing and evaluate the compliance of data sharing at the same time. If the compliance score meets the threshold of the sharing agreement, data sharing is allowed; otherwise, the sharing conditions need to be adjusted.

[0121] The data tracking and tracing module is used to track and trace the flow and usage history of data in real time during the sharing process. Use the distributed ledger of the blockchain to record each data access, modification, deletion, etc. Provide the access history tracing function of the data sharing party to ensure that all data usage behaviors can be transparently audited.

[0122] The data dynamic management module is used to dynamically manage the sharing rules of shared data according to the actual situation. Automatically adjust the data access permissions according to user behavior and data usage conditions, and dynamically adapt to different sharing requirements.

[0123] An anonymization processing module for de-identifying and anonymizing user personal information. It performs desensitization and anonymization processing, using technologies such as k-anonymity and differential privacy to ensure that the data cannot be directly associated with specific users. It automatically performs de-identification processing to ensure compliance with privacy protection standards, especially in cross-platform and cross-domain data sharing scenarios.

[0124] Through the collaboration of multiple functional modules, the system realizes efficient, secure, and compliant personal data sharing management. Each module plays a role in different stages of data sharing, from data encryption to permission management, and then to compliance assessment and privacy protection, ensuring that user data is always strictly protected during the sharing process and complies with relevant regulatory requirements.

[0125] Example Three:

[0126] This example also provides a computer device applicable to a situation of a personal data sharing management method based on big data, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the personal data sharing management method based on big data proposed in the above example.

[0127] This example also provides a storage medium with a computer program stored thereon, and when the program is executed by a processor, it implements the personal data sharing management method based on big data proposed in the above example.

[0128] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0129] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0130] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0131] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0132] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A personal data sharing management method based on big data, characterized in that: The following steps are involved: Personal data is encrypted and managed on blockchain; Automatically configure sharing permissions for personal data through smart contracts in the blockchain; After the smart contract has configured permissions, data sharing negotiations are carried out to reach a compliant sharing agreement; The data sharing negotiation includes: Joint computing is performed between multiple data providers in an encrypted manner. Each party can only see its own data. Before data sharing, all participants negotiate and confirm the conditions for sharing data through smart contracts, including the purpose of use, time range, and division of responsibilities. During the joint computing, a multi-party computing negotiation model is constructed to measure the data contribution of each participant and the rationality of shared data: In the shared scene, The parties participate in the data sharing negotiation and set The private data provided is , perform joint calculations and modeling, and set each party’s private data is a vector, and the dimension of each data vector is ,Right now ; Each party performs joint calculations through multi-party computing. During the calculation process, no party can directly know the data of other parties. The multi-party computing negotiation model includes a data contribution formula and a compliance evaluation formula; Data contribution formula, used to evaluate each participant’s contribution to shared data and privacy protection; A compliance assessment formula to quantify the shared data compliance of all parties; The data contribution formula is as follows: ； in, Indicates The data contribution of the party; Is the data dimension The weight given to the purpose of sharing, Indicates the importance of the data in this dimension; It is for Fangdi The privacy protection function of dimensional data uses differential privacy function and data encryption function; The compliance assessment formula is as follows: ； in, is the compliance score of the shared agreement, reflecting whether the multi-party negotiation meets the compliance standards; Each party The weight in the compliance agreement represents the influence of each party in the final negotiation; It is The data contribution of the party; It is The parties' commitment to the compliance of shared data. Indicates whether the party strictly follows the compliance protocol; If the compliance score , the system confirms that the data sharing agreement is established, and all parties can share data safely under the supervision of the smart contract; If the compliance score is below the threshold , the smart contract will prompt the need to adjust data sharing conditions or data privacy protection requirements; Based on the sharing protocol, decentralized data tracking and tracing are carried out; Dynamically manage data and the system automatically adjusts sharing rules.

2. According to the personal data sharing management method based on big data according to claim 1, it is characterized in that: In the blockchain management after the personal data is encrypted, it includes: First, the encryption algorithm AES-256 is used to encrypt personal data, and the encrypted personal data is managed using blockchain technology; Through smart contracts, access permissions for personal data are controlled and log records are managed. The ownership, usage rights and authorization information of personal data are recorded in real time. Any access, update or deletion of personal data is recorded in the blockchain, forming a transparent and tamper-proof access history.

3. According to the personal data sharing management method based on big data in claim 1, it is characterized in that: The smart contract automatically configures the sharing rights of personal data. The automatic configuration process includes: Users set access rights to their personal data, including limiting the circumstances and purposes under which specific parties can access personal data, and setting conditional restrictions based on the time and frequency of data access; Smart contracts automatically check permissions when data is accessed, ensuring that only authorized specific parties access users’ personal data; The permission configuration is automatically adjusted based on user behavior. If the user has not checked the data usage for a long time, the smart contract will automatically trigger the process of re-examining the permission settings.

4. According to the personal data sharing management method based on big data in claim 1, it is characterized in that: When dynamically managing data, the system automatically adjusts sharing rules based on different situations by analyzing user behavior, data usage, and relevant laws and regulations in real time; When the usage frequency of a data sharing party changes, the system automatically analyzes and prompts whether the relevant permissions need to be adjusted, and temporarily freezes data sharing in certain circumstances.

5. According to the personal data sharing management method based on big data in claim 1, it is characterized in that: The system also includes de-identification and anonymization of multi-dimensional data during data sharing; Use k-anonymity and differential privacy methods to de-identify users' personal information so that the data cannot be directly associated with personal identity; Establish an anonymization protocol to hide users’ personally identifiable information.

6. The personal data sharing management method based on big data according to claim 1 is characterized in that: The method includes the following functional modules: The blockchain management module is used to encrypt personal data and record the encrypted data on the blockchain; Smart contract management module, which is used to manage the permissions, rules and conditions for sharing personal data, and uses smart contracts for automated management; Data sharing negotiation module, which is used to protect privacy through multi-party computation and evaluate the compliance of data sharing; Data tracking and tracing module, used to track and trace the flow and usage history of data in the sharing process in real time; The data dynamic management module is used to dynamically manage the sharing rules of shared data according to actual conditions; Anonymization processing module, used for de-identification and anonymization of user personal information.

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