Personal data sharing management method based on big data

Abstract

The invention discloses a personal data sharing management method based on big data, which relates to the technical field of data management, and comprises a block chain management module, an intelligent contract management module, a data sharing negotiation module, a data tracking and tracing module, a data dynamic management module and an anonymization processing module. According to the personal data sharing management method based on the big data, through encryption and block chain management, the tampering resistance and transparency of the personal data are ensured, and the data leakage risk in a traditional storage mode is avoided. The dynamic management mechanism of the smart contract and the data can dynamically adjust the data sharing rule, and the flexibility and compliance in the data sharing process are improved. Multi-party calculation and compliance evaluation ensure privacy protection and rationality in data sharing, and collision or abuse behaviors generated in the sharing process of a plurality of participants are avoided. An efficient, safe, compliant and transparent solution is provided for personal data sharing, and meanwhile privacy rights and interests of users are protected.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a personal data sharing management method 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, data privacy and security issues are becoming increasingly serious, especially in cross-platform and cross-domain data sharing scenarios. How to ensure that personal data (such as personal medical and health data) can meet business needs and effectively protect user privacy during the sharing process has become a major challenge facing current technology.

[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, permission management in data sharing is usually static and difficult to adapt to complex and dynamic sharing needs; at the same time, when multiple parties share data, existing technologies are difficult to achieve data privacy protection and responsibility division for each party, which can easily lead to data leakage or abuse. At the same time, there are deficiencies in compliance assessment of data sharing and automated adjustment of permissions, which cannot meet rapidly changing sharing needs. Summary of the invention

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

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

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

[0007] Personal data is encrypted and managed on blockchain;

[0008] Automatically configure sharing permissions for personal data through smart contracts in the blockchain;

[0009] After the smart contract has configured permissions, data sharing negotiations are carried out to reach a compliant sharing agreement;

[0010] Based on the sharing protocol, decentralized data tracking and tracing are carried out;

[0011] Dynamically manage data and the system automatically adjusts sharing rules.

[0012] To further optimize this technical solution, the blockchain management of personal data after encryption includes:

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

[0014] 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.

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

[0016] 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;

[0017] Smart contracts automatically check permissions when data is accessed, ensuring that only authorized specific parties access users’ personal data;

[0018] 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.

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

[0020] Joint calculations are performed between multiple data providers in an encrypted manner, and 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 frame, and division of responsibilities.

[0021] To further optimize this technical solution, 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:

[0022] 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 ;

[0023] Each party performs joint calculations through multi-party computing. During the calculation process, no party can directly know the data of other parties.

[0024] Further optimizing the technical solution, the multi-party computing negotiation model includes a data contribution formula and a compliance assessment formula;

[0025] Data contribution formula, used to evaluate each participant’s contribution to shared data and privacy protection;

[0026] A compliance assessment formula to quantify the shared data compliance of all parties.

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

[0028] ;

[0029] in,

[0030] Indicates The data contribution of the party;

[0031] Is the data dimension The weight given to the purpose of sharing, Indicates the importance of the data in this dimension;

[0032] It is for Fangdi The privacy protection function of dimensional data uses differential privacy function and data encryption function;

[0033] The compliance assessment formula is as follows:

[0034] ;

[0035] in,

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

[0037] Each party The weight in the compliance agreement represents the influence of each party in the final negotiation;

[0038] It is The data contribution of the party;

[0039] It is The parties' commitment to the compliance of shared data. Indicates whether the party strictly follows the compliance protocol.

[0040] Further optimizing the technical solution, when dynamically managing data, the system automatically adjusts the sharing rules according to different situations by analyzing user behavior, data usage, and information on relevant laws and regulations in real time;

[0041] 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.

[0042] To further optimize the technical solution, the system also includes de-identification and anonymization of multi-dimensional data during data sharing;

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

[0044] Establish an anonymization protocol to hide users’ personally identifiable information.

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

[0046] The blockchain management module is used to encrypt personal data and record the encrypted data on the blockchain;

[0047] 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;

[0048] Data sharing negotiation module, which is used to protect privacy through multi-party computation and evaluate the compliance of data sharing;

[0049] Data tracking and tracing module, used to track and trace the flow and usage history of data in the sharing process in real time;

[0050] The data dynamic management module is used to dynamically manage the sharing rules of shared data according to actual conditions;

[0051] Anonymization processing module, used for de-identification and anonymization of user personal information.

[0052] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a personal data sharing management method 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 having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a personal data sharing management method 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 personal data sharing management method based on big data, which has the following beneficial effects:

[0055] This personal data sharing management method 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 dynamic management mechanism of smart contracts and data can dynamically adjust the rules of data sharing, improving the flexibility and compliance in the data sharing process. Multi-party computing and compliance assessment ensure privacy protection and rationality in data sharing, avoiding conflicts or abuses among multiple participants in 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 briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 A schematic diagram of the use process of a personal data sharing management method based on big data proposed by the present invention;

[0058] Figure 2 This is a structural schematic diagram of a personal data sharing management method based on big data proposed by the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] Embodiment 1:

[0063] Reference Figure 1 , which is the first embodiment of the present invention, and provides a personal data sharing management method based on big data, the system includes:

[0064] Personal data is encrypted and managed on blockchain

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

[0066] 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.

[0067] Automatically configure sharing permissions for personal data through smart contracts in the blockchain

[0068] In this embodiment, the user sets the access rights to his or her personal data. The access rights include limiting the circumstances and purposes under which a specific party can access the personal data, and setting conditional restrictions based on the time and frequency of data access.

[0069] Smart contracts automatically check permissions when data is accessed, ensuring that only authorized specific parties access users' personal data.

[0070] 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.

[0071] After the smart contract has configured permissions, data sharing negotiations are carried out to reach a compliant sharing agreement

[0072] In this embodiment, joint computing is performed between multiple data providers in an encrypted manner. Each party can only see its own data and cannot directly access the data of other parties to avoid information leakage. 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. In this way, different parties can share data safely while ensuring that the data is not leaked or abused during the sharing process. The rationality of the shared data by each party is calculated to determine whether a compliant sharing agreement has been reached.

[0073] Furthermore, 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:

[0074] 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 ;

[0075] Each party performs joint calculations through multi-party computing. During the calculation process, no party can directly know the data of other parties.

[0076] Through encrypted joint computing and smart contract negotiation mechanisms, it ensures that when participants 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 division of responsibilities: All parties can negotiate and confirm the purpose, time frame and other conditions of shared data use to ensure compliance.

[0079] Data rationality calculation: Based on specific algorithms and formulas, the rationality of the data shared by all parties is calculated to ensure that there is no data conflict or abuse.

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

[0081] Data contribution formula, used to evaluate each participant’s contribution to shared data and privacy protection;

[0082] A compliance assessment formula to quantify the shared data compliance of all parties.

[0083] The data contribution formula is as follows:

[0084] ;

[0085] in,

[0086] Indicates The data contribution of the party;

[0087] Is the data dimension The weight given to the purpose of sharing, Indicates the importance of the data in this dimension;

[0088] It is for Fangdi The privacy protection function of dimensional data adopts differential privacy function and data encryption function.

[0089] The compliance assessment formula is as follows:

[0090] ;

[0091] in,

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

[0093] Each party The weight in the compliance agreement represents the influence of each party in the final negotiation;

[0094] It is The data contribution of the party;

[0095] It is The parties' commitment to the compliance of shared data. Indicates whether the party strictly follows the compliance protocol.

[0096] When used, the model includes:

[0097] Smart contract negotiation before data sharing: Before data sharing, all participants 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 commitments are implemented through functions and The parties set the data sharing conditions according to the smart contract. and .

[0099] Calculation and encryption process: The data provider will provide the data vector Encryption and joint computing. Each party can only obtain the encryption result of its own data without directly accessing the private data of other parties.

[0100] In this process, the data contribution of each party is calculated. , and conduct compliance assessments .

[0101] Compliance assessment and sharing confirmation: After completing all data calculations and compliance assessments, the system will assess the contribution of all participants. and compliance Perform weighted summation to obtain compliance score .

[0102] If the compliance score , the system confirms that the data sharing agreement is established, and all participants can safely share data under the supervision of the smart contract.

[0103] If the compliance score is below the threshold , smart contracts will prompt the need to adjust data sharing conditions or data privacy protection requirements.

[0104] Ultimately, all parties’ sharing behavior will be subject to compliance assessment, with a compliance score Must be greater than a certain threshold Only then can a compliant data sharing agreement be reached.

[0105] Decentralized data tracking and tracing based on sharing protocols

[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. A decentralized data tracking mechanism is introduced, and the distributed ledger of the blockchain is used to record the flow and usage of each data. Every time the data is accessed or transmitted, a log is automatically generated through a 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 authority, and can raise objections or demand accountability for access behaviors that do not comply with regulations. The decentralized tracking method ensures the transparency of data flow, so that all participants can have a clear understanding of the use of data, and it cannot be tampered with.

[0107] Dynamically manage data, and the system automatically adjusts sharing rules

[0108] In this embodiment, 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.

[0109] 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.

[0110] Real-time audits can also be performed to record and analyze all data accesses, detect potential non-compliance behaviors in a timely manner, and issue warnings or automatic bans through smart contracts.

[0111] The system also includes de-identification and anonymization of multi-dimensional data during data sharing;

[0112] Adopt k-anonymity and differential privacy data desensitization technology to de-identify users' personal information, so that the data cannot be directly associated with personal identity;

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

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

[0115] Embodiment 2:

[0116] Reference Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a module composition of a 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. Smart contracts are used to manage data access permissions and access logs.

[0119] Smart contract management module is used to manage the permissions, rules and conditions for sharing personal data, and use smart contracts for automated management. Set different sharing permissions for personal data, such as sharing purpose, access party, time range, etc. Use smart contracts to automatically perform permission checks and adjustments based on the conditions set by the user. The system will evaluate in real time whether the rules meet the current data sharing needs, and automatically adjust 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. 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 the sharing process in real time. The distributed ledger of the blockchain is used to record each data access, modification, deletion and other operations. The access history tracing function is provided for 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 actual conditions. It automatically adjusts data access permissions based on user behavior and data usage, and dynamically adapts to different sharing needs.

[0123] Anonymization module is used to de-identify and anonymize user personal information. Desensitization and anonymization are performed, using technologies such as k-anonymity and differential privacy to ensure that data cannot be directly associated with specific users. De-identification is automatically performed to ensure compliance with privacy protection standards, especially in cross-platform and cross-domain data sharing scenarios.

[0124] The system achieves efficient, secure and compliant personal data sharing management through the collaboration of multiple functional modules. Each module plays a role in different stages of data sharing, from data encryption to rights management, 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] Embodiment three:

[0126] This embodiment also provides a computer device, which is applicable to 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 computer executable instructions to implement the personal data sharing management method based on big data proposed in the above embodiment.

[0127] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the personal data sharing management method based on big data proposed in the above embodiment is implemented.

[0128] The computer device may be a terminal, and the computer device 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 an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0129] If the 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0131] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0132] It should be understood that the 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0133] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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; 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: The blockchain management of personal data after encryption 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: The data sharing negotiation includes: Joint calculations are performed between multiple data providers in an encrypted manner, and 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 frame, and division of responsibilities.

5. A personal data sharing management method based on big data according to claim 4, characterized in that: 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.

6. A personal data sharing management method based on big data according to claim 5, characterized in that: 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.

7. A personal data sharing management method based on big data according to claim 6, characterized in that: 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.

8. The method for sharing and managing personal data based on big data according to claim 1, characterized in that: When dynamically managing data, the system automatically adjusts sharing rules based on different situations by analyzing user behavior, data usage, and information on 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.

9. The method for sharing and managing personal data based on big data according to claim 1, 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.

10. The method for sharing and managing personal data based on big data according to claim 1, 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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