A smart city data sharing system based on blockchain technology

Abstract

The present invention discloses a smart city data sharing system based on blockchain technology, which involves the intersection of medical data processing and information technology. The system includes the following modules: a data storage and blockchain mapping module, which uses a salted SHA-256 algorithm to generate a summary and store it on the blockchain. The original data is distributedly stored on IPFS with redundancy and caching, and the summary storage is regulated according to the popularity of the data; a smart contract-driven data sharing module, which subdivides the authorization hierarchy, establishes pre-request verification, dynamically updates rules, and automatically executes functions to ensure compliance and controllability; a privacy protection technology integration module, which expands the application of zero-knowledge proof and uses hybrid encryption to optimize multi-party secure computing; and a data traceability and compliance audit module, which records logs to form a historical chain, is traceable, and outputs audit reports. This invention can comprehensively improve the security, efficiency, and compliance of medical data sharing and has broad application prospects.

Description

Technical Field

[0001] The present invention relates to the intersection of medical data processing and information technology, and in particular to a smart city data sharing system based on blockchain technology. Background Art

[0002] In recent years, the healthcare industry has undergone profound transformation, with medical data sharing becoming a key driver of medical advancement. As the pace of medical informatization accelerates exponentially, the volume of medical data has surged, encompassing a vast and diverse array of information, including patient medical records, test reports, and imaging data. This data holds immense value. Effectively sharing it can, on the one hand, provide comprehensive insights for clinical diagnosis, empowering doctors to transcend geographical and institutional constraints, integrate multi-source information, and develop precise treatment plans, reducing the risk of misdiagnosis and missed diagnoses and improving the quality of healthcare services. Furthermore, for medical research, rich data samples are the cornerstone of scientific innovation, accelerating the development of new drugs, exploring disease mechanisms, and paving the way for addressing challenging conditions.

[0003] In the traditional medical data sharing model, data storage and management architecture are primary pain points. Most medical institutions use centralized storage, concentrating massive amounts of medical data in a single data center. This architecture has drawbacks. Data integration and storage are highly centralized, lacking risk mitigation mechanisms, resulting in fragile security and a high risk of privacy breaches. Internal misconduct is particularly difficult to prevent, with some individuals motivated by profit secretly selling data, further exacerbating the privacy crisis. Furthermore, centralized storage is struggling to cope with the explosive growth of data. Storage costs are skyrocketing, and the costs of purchasing and maintaining large storage equipment and continuously expanding capacity are overwhelming medical institutions. Furthermore, retrieval and access efficiency are low, with large amounts of data piling up in one place, resulting in slow query responses and severely disrupting the normal operation of medical services.

[0004] The implementation of data-sharing rules is also plagued by numerous problems. Currently, the process primarily relies on manual management, with complex and cumbersome steps from data owner authorization to user access. Collaboration between medical institutions requires significant time and effort to negotiate authorization details and sign agreements, and the lack of unified, transparent regulations makes it easy for illegal sharing to occur. Vague definitions of data usage permissions by different institutions lead to authorization confusion, resulting in some data being used beyond its scope and legitimate needs being hindered by protracted processes. This makes it difficult to meet the strict compliance requirements for medical data sharing, significantly hindering the deep mining and full realization of the value of medical data.

[0005] The emergence of blockchain technology, with its decentralized, tamper-proof, and traceable properties, offers a glimmer of hope for overcoming the challenges of medical data sharing. It transcends the drawbacks of traditional centralized storage by distributing data across numerous nodes, reducing the risk of single points of failure and strengthening privacy protection. Smart contract mechanisms promise to automate the execution of sharing rules, improving transparency and efficiency. However, existing blockchain-based medical data sharing solutions remain largely theoretical or in the initial stages of implementation, lacking a comprehensive and in-depth integration of blockchain with the complex business needs of medical data. Data storage optimization is often insufficient, leading to redundant and inefficient blockchain storage; privacy protection methods are limited, making them inadequate for the highly sensitive nature of medical data; and smart contracts are rudimentary, unable to adapt to diverse authorization scenarios and dynamic rule adjustments.

[0006] This invention targets these pain points, conducts in-depth research, and is committed to creating an innovative and complete blockchain-based medical data sharing system to comprehensively overcome existing difficulties and open up a new path for medical data sharing. Summary of the Invention

[0007] This invention aims to overcome the shortcomings of existing technologies and provide an innovative blockchain-based medical data sharing system. By integrating cutting-edge technologies such as blockchain and cryptography, it can achieve "secure storage, efficient sharing, privacy protection, and full traceability of medical data," thereby improving the quality of medical services, promoting the development of medical research, and safeguarding the legitimate rights and interests of all parties.

[0008] Data storage and blockchain mapping module: 1) Data hierarchical storage structure: (1) It has a data summary generation and storage submodule, which uses the salted SHA-256 hash algorithm to generate data summaries. The salt value is generated based on the unique identifier of the medical institution and the precise timestamp of data generation to ensure the uniqueness and stability of the summary. It is stored on the blockchain in the form of a structure containing the data hash value, data source, generation time and original data index identifier; (2) It also includes a raw data distributed storage submodule, which relies on IPFS to store raw medical data, uses content-based hashes as data identifiers to ensure retrieval efficiency, and builds a local cache index mechanism to improve retrieval speed. It also implements a multi-copy redundant storage strategy to back up data in multiple geographically dispersed IPFS nodes, and verifies the data through the verification function Verif ycopy (D original , D copy ) Verify the consistency of the copies to ensure data integrity. 2) Blockchain storage optimization function: Introduce the data heat factor h, combine the amount of new data per unit time n, and the current available storage space s of the blockchain, and optimize the storage cost function C store (n, s, h) dynamically adjusts the data summary storage strategy. When the data is hot, the summary is stored immediately. Otherwise, the delay time T is calculated according to the remaining time and data growth. delay=(s threshold -s) / n*k(s threshold is the storage threshold, k is the adjustment coefficient) delay storage; also use the summary storage priority scoring function Score priority (D)=w1*e+w2*(1 / t valid )+w3*l privacy (e is the urgency, t valid For validity period, privacy is the privacy level, w1, w2, and w3 are weight coefficients) and are scored and sorted according to data characteristics, with key data summaries being stored first.

[0009] Smart contract driven data sharing module: 1) Smart contract architecture: (1) Contains data owner contract, breaks through the limitations of traditional authorization, and can subdivide the authorization level, such as setting the permission of "can comment but cannot modify", through the function Grant multi (ID user ,D,l auth ) authorization and record the authorization information (ID user ,D,l auth 、T auth 、T expire ) in the contract list, and is equipped with an authorization revocation mechanism, function Revoke auth (ID user ,D) can delete authorization and notify users according to the owner's instructions; (2) also includes data user contract, pre-request verification link, function PreVerif yuser (ID user , ID data ) Screen the user's preliminary authorization qualifications according to the whitelist, cooperation agreement, etc., and only after passing can a formal request be initiated. And every time the user completes a key operation, the function Feedback use (O type , D, T op ) Feedback details to the contract independent log area; (3) and shared rule contracts, build dynamic rule adjustment mechanism, function Update rule (newRuleSet, T eff ) Update the rule set newRuleSet in a timely manner based on laws, policies, and recent event feedback, and push notifications to all parties before it takes effect. It also supports cross-institutional customization of rules, and the function Adapt inst (ID inst , rule inst ) Embedded organization-specific rules inst (ID inst 2) Automatic execution function: (1) Request verification process optimization function E share(req, rule), multi-dimensional verification of user access requests. In addition to routine identity and data identification verification, for scientific research and other scenarios, it links with external scientific research management systems to verify project records, ethical compliance and other information. When abnormal cross-regional requests, massive downloads in a short period of time and other risks are found during the verification, the function Warn is used. risk (req, riskType) to warn the owner and administrator; (2) It also includes access control execution function, which adopts progressive authorization strategy in the face of complex data interaction requirements. Function Progressive grant (reqStage, ID user ,D) Grant adaptation permissions according to the project stage, and set up a timeout control mechanism. When the function opens the access channel, the timer is started. When the specified time T is exceeded, timeout If the operation is not completed, the channel will be automatically closed using the Ttimeout function. control (ID user , D) Record timeout event notification and troubleshooting.

[0010] Privacy protection technology integration module: 1) Zero-knowledge proof application model: (1) Includes complex assertion proof support function, for complex assertions such as "patients aged 30-50 years old and suffering from specific chronic diseases, no major surgery in the past year" complex , extended proof generation function G zkp (P complex , D) Disassemble the assertion into sub-assertions and generate the corresponding zero-knowledge proof components π1, π2, ..., π n , data users pass the verification function V zkp ((π1,π2,…,π n ), P comple x) Verify the sub-proof according to the assertion logic; (2) It also has the function of proof validity extension and reuse, building a proof reuse mechanism. After the data owner generates the zero-knowledge proof π of the assertion P, it is stored in a specific area of the blockchain and has an associated validity period. With scene label T agscene , other users in the same scenario and within the validity period can reuse the verification function R zkp (π, P, Tag scene ) Rapid reuse confirmation, and in long-term tracking scenarios such as chronic disease management, the link function Link zkp (π prev ,π next ) Connect the proofs of each stage in series, and the user can trace back the data verification trajectory. 2) Homomorphic encryption and multi-party secure computing collaborative model: (1) It has the function of adaptive optimization of encryption algorithm. In view of the differences in medical data types, a hybrid encryption algorithm is adopted. Text data is encrypted with BFV algorithm to ensure basic security and frequent computing requirements. Image data is encrypted with FHEW algorithm to enhance privacy protection. Through dynamic encryption selection function Selectenic (D type ) automatically switches according to the data type and the number of participating institutions n share , real-time security threat level threatLevel (assessed by the security monitoring system), through the parameter adjustment function Adjust param (n share , threatLevel) real-time optimization of encryption parameters; (2) It also includes the multi-party secure computing process refinement function, before starting the multi-party secure computing protocol, strengthen the identity authentication of the participants, and the multi-factor authentication function Auth mpc (ID inst ,Key pub ,Bio feat ) Comprehensive verification agency identity ID inst , public key pub and biometrics (pre-stored in blockchain), after jointly calculating the encrypted statistical result E(S), the result verification function Verify mpc (E(S), rule comp ) Verify according to preset rules and expected result range, and audit function Vudit if abnormal mpc (E(S), Log comp ) Backtracking calculation log Log comp Troubleshoot the problem.

[0011] Data traceability and compliance audit module: 1) Operation log record model: Generate a log file containing the operation time T for each data operation on the blockchain. op ,,Operation subject identity ID op (owner, user, etc.), operation object ID obj (Data summary or original data identification) and operation details description Desc op Log records, with the help of log storage function S log (L) Store in chronological order to form an unchangeable operation history chain. 2) Traceability and audit function: With traceability function T race (ID obj , T start , T end ), based on a given data object identifier and time interval, accurately retrieve past operations from the blockchain log and restore the data flow path; also includes compliance audit function A udit (rule,L set ), based on the compliance rule set rule and the operation log set L set Review the logs and output an audit report covering the number and type of illegal operations and the entities involved.

[0012] The advantages of the present invention are:

[0013] 1. Innovative data tiered storage combined with blockchain optimization strategies effectively balances data integrity verification with storage resource limitations, significantly improving system scalability, reducing storage costs, and adapting to the rapid growth of medical data.

[0014] 2. A sophisticated smart contract system covers multi-role and multi-level authorization management, and cooperates with automatic execution functions to achieve automation and transparency of data sharing processes, significantly improve sharing efficiency, reduce human errors and illegal operations, and protect the rights and interests of data owners.

[0015] 3. The integration of cutting-edge multiple privacy protection technologies provides precise protection for all aspects of data sharing, whether it is complex assertion verification, real-time consultation interaction, or long-term data tracking and analysis, ensuring the privacy security of sensitive medical data and overcoming the industry's privacy challenges.

[0016] 4. The powerful logging, tracing and auditing functions based on blockchain give the system the ability to supervise data throughout the entire process. The data flow path is clear and traceable, and illegal operations are nowhere to hide, which greatly enhances the credibility of the system and meets the strict compliance requirements of the medical industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention.

[0018] Figure 1 : Overall architecture diagram of the medical data sharing system based on blockchain.

[0019] Figure 2 : Functional diagram of data storage module.

[0020] Figure 3 : Functional diagram of the privacy protection module.

[0021] Figure 4 : Functional diagram of the smart contract module.

[0022] Figure 5 : Functional diagram of data traceability and audit module.

[0023] Figure 6 :Detailed steps of the system data sharing process.

[0024] Figure 7 :Schematic diagram of zero-knowledge proof application model.

[0025] Figure 8 : Schematic diagram of the collaborative model of homomorphic encryption and multi-party secure computing.

[0026] Figure 9 : Interaction diagram of key links in privacy protection.

[0027] Figure 10 : Schematic diagram of dynamic adjustment of data storage.

[0028] Figure 11 : Smart contract rule update trigger diagram. DETAILED DESCRIPTION

[0029] Implementation Example 1

[0030] Attachment Figure 1-5 The system demonstrates the overall architecture of a blockchain-based medical data sharing system and functional diagrams of its various modules. Data generators, such as medical institutions, accessing the system must ensure a stable and reliable network connection. This can be monitored by periodically sending heartbeat packets. If a network interruption occurs, the system automatically initiates a reconnection attempt, attempting to reestablish the connection. The number of retries can be set to three, with gradually increasing intervals between each attempt, such as 10 seconds for the first attempt, 20 seconds for the second attempt, and 30 seconds for the third attempt.

[0031] Configure a data digest generation tool locally and strictly follow the salted SHA-256 algorithm to generate data digests. The salt value should be generated in conjunction with the medical institution's unique identifier and a millisecond-accurate timestamp to ensure the uniqueness and stability of the digest. For example, the medical institution's unique identifier can be a code assigned by the health department. A millisecond-accurate timestamp effectively prevents duplicate digest generation.

[0032] Upload the original data to the IPFS distributed storage network. During the upload process, the upload progress is monitored in real time, with a progress bar or percentage display to let users know the status of the upload. If an upload is interrupted, the system automatically retries the upload. The number of retries can be set based on the actual situation, for example, up to five retries. Furthermore, to improve upload efficiency, you can use a block upload method, splitting large files into smaller blocks for upload. Each block is marked as successfully uploaded, so that in the event of an interruption, only the unsuccessful blocks need to be re-uploaded.

[0033] Institutional information is registered on the blockchain. This information should include details such as the institution's name, address, contact information, and qualifications. During the registration process, this information is encrypted to ensure its security and integrity. Advanced encryption algorithms, such as the national SM2 algorithm, can be used to encrypt registration information and prevent tampering or theft.

[0034] Create an index link with the data summary PreVerify user (ID user , ID data The link establishment process should be secure and reliable to ensure the accuracy and stability of the link. A hash algorithm can be used to process the data summary to generate a unique index identifier, which can then be associated with the institution information to establish the index link.

[0035] Implementation Example 2

[0036] Attachment Figure 6 The detailed step diagram of the system data sharing process is shown. When a data user has data access requirements, the pre-request verification function PreVerify is first used under the data user contract framework. user (ID user , ID data ) Verify their own qualifications. The verification process should include verifying the user's identity and reviewing the rationality of the purpose of data use. For example, the authenticity and validity of the user's identity information can be verified by comparing it with the user database. At the same time, the purpose of use statement submitted by the user should be reviewed to ensure that the purpose of use complies with laws and regulations and medical industry standards.

[0037] If verification is successful, a formal request is sent to the data owner. The request should include a detailed description of the intended use, required data identifiers, and other information. The intended use should be specific, such as for research on a specific disease or clinical diagnosis. The required data identifiers should be accurate to the specific data field or dataset, allowing the data owner to accurately determine whether to authorize access.

[0038] After receiving the request, the data owner uses the Grant function in the data owner contract according to the sharing rule contract and the current authorization status. multi (ID user ,D,l auth ) to make authorization decisions. The authorization decision-making process should comprehensively consider multiple factors, such as the sensitivity of the data, the user's credibility, and the urgency of the intended use. For example, if the data involves sensitive patient information, such as ID numbers or medical records, authorization decisions should be made with greater caution. However, if the user's credibility is high, or the intended use is extremely urgent, such as saving a patient's life, authorization conditions can be appropriately relaxed.

[0039] If authorization is granted, the authorization information is recorded on the blockchain. This recording process should ensure the accuracy and immutability of the information. Blockchain's distributed ledger technology can be used to store authorization information on multiple nodes, ensuring consistency through a consensus mechanism. Furthermore, the authorization information should be encrypted to prevent theft or tampering.

[0040] Notify the user of the authorization results. This notification can be delivered via various methods, such as internal messaging, email, and text messages, to ensure that the user receives the authorization results promptly. The notification should include key information such as authorization details and expiration date, allowing the user to plan their data usage accordingly.

[0041] The user automatically executes function E based on the authorization share(req, rule) access data. During access, the scope of authorization and usage rules must be strictly adhered to, and data usage must not exceed the scope of authorization. At the same time, the system should monitor data access in real time, such as access time, number of accesses, and access locations, to ensure the safe use of data.

[0042] Each time a key operation is completed, such as 50% of data download is completed, a stage of data analysis is completed, etc., the function Feedback is used use (O type , D, T op ) Real-time feedback of operation details to the independent contract log area. Feedback information should include detailed information such as operation type, operation object, and operation time, so that data owners can promptly understand the usage of data.

[0043] Implementation Example 3

[0044] Attachment Figure 7-11 The implementation details of privacy protection in the data sharing process are shown. In data verification scenarios, such as when a telemedicine consultation needs to verify some of the patient's data features, if it is a complex assertion verification, after the doctor initiates the request, the system backend generates a function G through proof. zkp (P complex D) Rapidly generate sub-assertion proof components. The generation process should utilize efficient algorithms and parallel computing techniques to ensure the speed and accuracy of proof generation. For example, a distributed computing framework can be used to split complex assertions into multiple sub-assertions, distribute them to multiple computing nodes for simultaneous proof generation, and then merge and verify the generated sub-proof components.

[0045] Data users pass the verification function V in a short time zkp ((π1,π2,…,π n ), P complex ) receives and verifies the results. The verification process should strictly follow the zero-knowledge proof algorithm and protocol to ensure accuracy and privacy. For example, interactive verification of zero-knowledge proofs can be used to verify the validity of sub-proof components through multiple interactions, while ensuring that no sensitive information is leaked during the verification process.

[0046] If it is a simple real-time indicator verification, such as confirming the patient's temperature range, through the interactive function I zkp (P, D, rounds) Efficiently complete verification within the specified number of interaction rounds. The number of interaction rounds should be adjusted based on the importance and urgency of the data. For example, for urgent medical situations, such as saving a patient's life, fewer interaction rounds can be set to complete verification as quickly as possible; for general medical situations, more interaction rounds can be set to ensure verification accuracy.

[0047] When multiple institutions jointly conduct medical data statistical analysis, each institution first uses the multi-factor authentication function Auth mpc (ID inst ,Key pub ,Bio feat ) to complete identity authentication. The authentication process should employ a strict identity authentication mechanism to ensure the legitimacy and security of participating institutions. For example, a public key infrastructure (PKI)-based identity authentication method could be used, combining multiple factors such as the institution's digital certificate, public key, and biometrics to ensure accuracy and reliability.

[0048] After authentication is passed, text data is encrypted using the BFV algorithm, and image data is encrypted using the FHEW algorithm, depending on the data type, before participating in the joint computation. The encryption process should utilize advanced encryption algorithms and technologies to ensure data security and privacy. For example, homomorphic encryption can be used to encrypt data before conducting the joint computation, eliminating the need to decrypt the data during the computation process, ensuring data privacy.

[0049] The calculation is completed and the result verification function is Verify mpc (E(S), rule comp ) Verify the results. The verification process should be carried out strictly in accordance with pre-set rules and algorithms to ensure the accuracy and reliability of the calculation results. For example, digital signature technology can be used to verify the signature of the calculation results to ensure their integrity and authenticity.

[0050] Abnormal audit function Audit mpc (E(S), Log comp ) Retrospective troubleshooting. The troubleshooting process should utilize detailed audit logs and data analysis techniques to ensure that problems can be quickly located and fixed. For example, a distributed audit log system can be used to record and audit every step of the computation process, enabling rapid retrospective troubleshooting when anomalies occur.

[0051] Implementation Example 4

[0052] If the regulatory authorities or data owners have doubts about the use of certain data, the traceability function T race (ID obj , T start , T end ), enter the data identifier and the suspicious time period, and the system will quickly retrieve all operation records of the data during this period from the massive log records stored on the blockchain. The retrieval process should use efficient indexing technology and query algorithms to ensure that relevant records can be retrieved quickly and accurately. For example, inverted indexing technology can be used to index log records, so that relevant records can be quickly located after entering the data identifier and time period.

[0053] Completely restore the data flow path to provide strong support for investigations. The restoration process should utilize visualization and data analysis techniques to organize and analyze retrieved operation records, graphically displaying the data flow path. For example, a flow chart or time sequence diagram could be used to illustrate the entire process from data generation to use, allowing regulators or data owners to clearly understand the data flow.

[0054] Periodically (e.g. monthly) or when a specific event triggers (e.g. data leakage risk warning), the system will perform audits based on the compliance audit function A. udit (rule,L set ), automatically generating audit reports by combining pre-set compliance rules with a collection of operation logs over a period of time. The report generation process should utilize automation and data visualization techniques to ensure report accuracy and readability. For example, machine learning algorithms can be used to analyze operation logs to automatically identify illegal operations and risky behaviors, and then present them in the audit report in the form of charts, allowing regulators or data owners to quickly understand the system's compliance status.

[0055] The report details the number and type of violations, as well as the organizations or individuals involved, providing a basis for subsequent rectification and accountability, ensuring the system's continued compliance. The report should include a detailed description of the violations, an analysis of the causes, and recommendations for rectification, enabling regulators or data owners to take effective measures for rectification and accountability, ensuring the system's continued compliance.

Claims

1. A smart city data sharing system based on blockchain technology, characterized by: Contains: data storage and blockchain mapping module, smart contract-driven data sharing module, privacy protection technology integration module and data traceability and compliance audit module: The data storage and blockchain mapping module can realize hierarchical storage and blockchain mapping management of medical data, and includes: a data hierarchical storage structure and a blockchain storage optimization function component; the data hierarchical storage structure has a data summary generation and storage submodule, which uses a salted SHA-256 hash algorithm to generate a data summary, and the salt value is generated based on the unique identifier of the medical institution and the precise timestamp of data generation to ensure the uniqueness and stability of the summary, which is stored on the blockchain in the form of a structure containing a data hash value, a data source, a generation time and an original data index identifier; the data hierarchical storage structure includes a raw data distributed storage submodule, which relies on IPFS to store raw medical data, uses content-based hashes as data identifiers to ensure retrieval efficiency, and at the same time builds a local cache index mechanism to improve retrieval speed, and implements a multi-copy redundant storage strategy to back up data at multiple geographically dispersed IPFS nodes, and through the verification function Verify the consistency of copies to ensure data integrity; The blockchain storage optimization function is expressed as follows: introducing the data heat factor h, combining the amount of new data per unit time n, and the current available storage space s of the blockchain, through the storage cost optimization function Dynamically adjust the data summary storage strategy. When the data is hot, the summary is stored immediately. Otherwise, the delay is calculated based on the remaining time and data growth. Delayed storage, where is the storage threshold, k is the adjustment coefficient; the blockchain storage optimization function uses the summary storage priority scoring function Sorting by data characteristics, giving priority to storing key data summaries; where e is the urgency, For validity period, is the privacy level, is the weight coefficient; The smart contract driven data sharing module can realize the sharing process under the control of medical data smart contract, including: smart contract architecture and automatic execution function components; the smart contract architecture includes data owner contract, breaking through the limitations of traditional authorization, subdividing the authorization level, and Authorize and record authorization information In the contract list, it is also equipped with an authorization revocation mechanism, function Authorization can be deleted and users can be notified according to the owner's instructions; the smart contract architecture includes data user contract, pre-request verification link, function Screen the user's preliminary authorization qualifications according to the whitelist and cooperation agreement. Only after passing can a formal request be initiated. And each time the user completes a key operation, the function Feedback details to the independent log area of the contract; the smart contract architecture can build a dynamic rule adjustment mechanism, function Update the rule set in a timely manner based on laws, regulations, policies, and recent event feedback And push notification to all parties before it takes effect, and support cross-institutional customization of rules, functions Embed institution-specific rules ; The automatic execution function has a request verification process optimization function ,Multi-dimensional verification of user access requests, when abnormal cross-regional requests and massive download risks in a short period of time are found during the verification, the function To warn the owner and administrator; the automatic execution function includes the access control execution function, which adopts a progressive authorization strategy in the face of complex data interaction requirements. Grant adaptation permissions according to the project stage, and set up a timeout control mechanism. When the function opens the access channel, the timer is started. Unfinished operation, automatically close the channel, function Record timeout event notification and troubleshooting; The privacy protection technology fusion module is used to ensure the privacy security of medical data during sharing, including: zero-knowledge proof application model and homomorphic encryption and multi-party secure computing collaborative model components; the zero-knowledge proof application model includes complex assertion proof support function, for complex assertions , extended proof generation function Disassemble the assertion into sub-assertions and generate corresponding zero-knowledge proof components , data users pass the verification function Verify the sub-proof according to the assertion logic; the zero-knowledge proof application model has the function of proof validity extension and reuse, and builds a proof reuse mechanism. After the data owner generates the zero-knowledge proof π of the assertion P, it is stored in a specific area of the blockchain and associated with the validity period. With scene tags ; Other users are meeting the scenario Matches and the current time is Under the conditions within, by reusing the verification function Rapid reuse confirmation, and in the long-term tracking scenario of chronic disease management, the link function is used By connecting the proofs of each stage in series, users can trace back the data verification trajectory; the homomorphic encryption and multi-party secure computing collaborative model has the function of adaptive optimization of encryption algorithm. In view of the differences in medical data types, a hybrid encryption algorithm is adopted. Text data is encrypted with BFV algorithm to ensure basic security and frequent computing requirements. Image data is encrypted with FHEW algorithm to enhance privacy protection. Through dynamic encryption selection function Automatically switch based on data type and number of participating institutions , real-time security threat level , optimize encryption parameters in real time through parameter adjustment function; the homomorphic encryption and multi-party secure computing collaborative model includes multi-party secure computing process refinement function, strengthens the identity authentication of participants before starting the multi-party secure computing protocol, and multi-factor authentication function Comprehensive verification agency identity , public key and biometrics , joint calculation to obtain encrypted statistical results After that, the result verification function Verify according to preset rules and expected result range, and audit function if abnormal Backtracking calculation log Troubleshooting; The data traceability and compliance audit module is used to achieve traceability and compliance audit of the entire life cycle of medical data, including: an operation log recording model and a traceability and audit function component; The data traceability and compliance audit module includes an operation log recording model: a data log containing the operation time is generated for each data operation on the blockchain. , identity of the operating entity , Operation object identifier and detailed description of the operation Log records, with the help of log storage function Stored in chronological order, forming an unchangeable operation history chain; the traceability and audit function: has a traceability function , according to the given data object identifier and time interval, accurately retrieve the operation history from the blockchain log and restore the data flow path; also includes compliance audit function , according to the compliance rule set and operation log collection Review the logs and output an audit report covering the number and type of illegal operations and information on the entities involved.

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