Data sharing system and method based on privacy protection technology

Abstract

The invention discloses a data sharing system and method based on a privacy protection technology, and relates to the technical field of data sharing, in the system, a data encryption module is used for performing encryption processing on input data of at least one participant by using an encryption algorithm to obtain encrypted data; the secure multi-party computing protocol module is used for receiving encrypted data from a plurality of participants and executing secure multi-party computing on the premise of not decrypting to obtain a joint computing result; the homomorphic encryption calculation module is used for executing calculation operation on encrypted data from a single participant in an encryption state to obtain an encryption calculation result; and the user interface module is used for providing a data input function and a calculation result output function. According to the method, the privacy protection capability is enhanced, the data processing efficiency is improved, the management process is simplified, and the limitations of a traditional scheme in the aspects of data security, computing power, access control and the like are effectively dealt with.

Description

Technical Field

[0001] This invention relates to the field of data sharing technology, and in particular to a data sharing system and method based on privacy protection technology. Background Technology

[0002] Modern data-intensive applications, such as cross-institutional financial risk control, multi-center medical research, and collaborative legal services, have created an urgent need for efficient, secure, and flexible data sharing. These applications aim to fully leverage the value of data elements while strictly protecting data privacy and security, and supporting multi-party collaboration on a foundation of mutual distrust. However, achieving efficient data sharing while protecting privacy remains a significant technical challenge.

[0003] Currently, traditional technical solutions typically rely on centralized database systems for data management and sharing. In this approach, data security and privacy protection largely depend on single security measures, such as data transmission encryption and network firewalls. Data is centrally stored on a central server, and participants need to transmit raw data to this central node for computation or analysis. Access control is usually managed based on roles or simple rules.

[0004] This traditional approach has several drawbacks. Due to centralized data storage, an attack on the central database poses a widespread threat to the data security of the entire system. Regarding data sharing, traditional approaches suffer from trust issues, as control of the data is concentrated in the hands of a single entity, limiting the breadth and depth of data sharing. In terms of computing power, traditional approaches typically require data to be transferred to a central server or a specific local environment for processing, which not only increases the risk of data leakage during transmission but also limits the flexibility and efficiency of distributed computing. For scenarios requiring multi-party collaboration, traditional approaches are often difficult to implement because data sharing and computation require complete trust, which is difficult to achieve among competing or privacy-conscious parties. Regarding access control, traditional approaches may lack fine-grained permission management, resulting in imprecise allocation of data access permissions and increasing the risk of internal data misuse and leakage. Furthermore, traditional approaches have low technical integration; the compatibility and collaborative capabilities between different components such as data preprocessing, encryption, computation, and auditing are limited, affecting the stability of the entire system and user experience. These limitations of traditional technical solutions in data sharing, privacy protection, computing power, data security, access control, and technical integration make it difficult to meet the needs of modern applications.

[0005] Therefore, there is an urgent need for an innovative technological solution that can technically ensure data privacy while supporting efficient and flexible multi-party and non-competitive data sharing to address the aforementioned challenges. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the shortcomings of the prior art, and specifically provides a data sharing system and method based on privacy protection technology, as detailed below: 1) In a first aspect, the present invention provides a data sharing system based on privacy protection technology, the specific technical solution of which is as follows: It includes a data encryption module, a secure multi-party computation protocol module, a homomorphic encryption computation module, and a user interface module; The data encryption module is used to: encrypt the input data of at least one participant using an encryption algorithm to obtain encrypted data; The secure multi-party computation protocol module is used to: receive encrypted data from multiple participants and perform secure multi-party computation without decryption to obtain the joint computation result; The homomorphic encryption computation module is used to: perform computation operations on encrypted data from a single participant in an encrypted state to obtain the encrypted computation result; The user interface module is used to provide data input and calculation result output functions.

[0007] The beneficial effects of the data sharing system based on privacy protection technology provided by this invention are as follows: The data encryption module encrypts the input data from participating parties, obtaining encrypted data. This ensures the confidentiality of data in subsequent processes from the source, reducing the risk of large-scale leakage from centralized storage. The secure multi-party computation protocol module receives encrypted data from multiple participating parties and performs secure multi-party computation without decryption, obtaining joint computation results. This enables multiple parties who do not trust each other to collaborate securely, solving the trust barrier problem in data sharing in traditional solutions and expanding the breadth and depth of data sharing. The homomorphic encryption computation module performs computation operations on encrypted data from a single participating party in an encrypted state, obtaining encrypted computation results. This enables direct data processing in an encrypted state, avoiding the efficiency bottlenecks and additional risks caused by data transmission and decryption to a central server, and improving the flexibility and efficiency of computation. The user interface module provides standardized data input and computation result output functions, simplifying user operations and improving the smoothness of technology integration and user experience. Through the collaboration of the above modules, the overall system enhances privacy protection capabilities, improves data processing efficiency, and simplifies management processes, effectively addressing the limitations of traditional solutions in terms of data security, computing power, and access control.

[0008] Based on the above solution, the data sharing system based on privacy protection technology of the present invention can be further improved as follows.

[0009] Furthermore, it also includes a blockchain integration module, which is used to record the joint computation results and encrypted computation results in the form of transactions in a distributed ledger, and to verify the integrity and source of the computation results based on the blockchain consensus mechanism.

[0010] The beneficial effects of adopting the above-mentioned further solution are as follows: The blockchain integration module records the joint computation results and encrypted computation results in the form of transactions in the distributed ledger, and verifies the integrity and source of the computation results based on the blockchain consensus mechanism. This technical feature provides immutable and transparent evidence of the computation results, effectively enhancing the credibility and auditability of the results. Distributed ledger storage avoids the risk of data corruption or tampering caused by attacks on a single centralized storage point. The blockchain consensus mechanism ensures decentralized and trustworthy collaboration in the recording process, enabling any participant to independently verify the integrity and authentic source of the computation results, thereby establishing a reliable data verification foundation in multi-party collaboration scenarios lacking a centralized trusted entity.

[0011] Furthermore, it also includes an access control module, which is used to perform fine-grained access control on the computation results stored in the blockchain integration module according to preset permission rules.

[0012] The beneficial effects of adopting the above-mentioned further solution are: the access control module performs fine-grained access control on the computation results stored in the blockchain integration module according to preset permission rules. This technical feature enables precise and dynamic management of access permissions for joint computation results and encrypted computation results. Fine-grained access control ensures that only explicitly authorized users or systems can access specific computation results, effectively preventing data abuse and unauthorized access after sharing. It solves the problems of coarse permission allocation and inflexible control in traditional solutions, and can adapt to complex and ever-changing business scenarios and access needs. Thus, while data is widely shared, it strengthens the last line of defense and improves the security and compliance level of the entire data sharing system.

[0013] Furthermore, it also includes a data preprocessing module, which is used to clean, standardize, de-identify and de-identify all input data, and provide the processed data to the data encryption module for encryption to obtain encrypted data.

[0014] The beneficial effects of adopting the above-mentioned further solutions are: Firstly, it improves the quality and consistency of input data from the source, providing a reliable foundation for subsequent encryption and computation. Secondly, cleaning and standardization eliminate errors and heterogeneity in the data, ensuring the accuracy of the computation results. Thirdly, de-identification and de-identification processes reduce the risk of privacy leaks before the data enters the encryption process, enhancing the protection of personal information and helping the entire data sharing process meet the compliance requirements of relevant data protection regulations.

[0015] 2) Secondly, the present invention also provides a data sharing method based on privacy protection technology, the specific technical solution of which is as follows: The input data of at least one participant is encrypted using an encryption algorithm to obtain encrypted data. It receives encrypted data from multiple participants and performs secure multi-party computation without decryption to obtain the joint computation result; In an encrypted state, computational operations are performed on encrypted data from a single participant to obtain the encrypted computation result; The system receives data input from at least one participant through the data input function and outputs joint calculation results and / or encrypted calculation results through the calculation result output function.

[0016] Based on the above scheme, the data sharing method of the present invention based on privacy protection technology can be further improved as follows.

[0017] Furthermore, it also includes: recording the joint computation results and encrypted computation results in the form of transactions in a distributed ledger, and verifying the integrity and source of the computation results based on the blockchain consensus mechanism.

[0018] Furthermore, it also includes: fine-grained access control of the stored calculation results according to preset permission rules.

[0019] Furthermore, before encrypting the input data of at least one participant using an encryption algorithm, the process also includes cleaning, standardizing, and de-identifying and de-identifying all input data.

[0020] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so as to enable the electronic device to implement any of the above-mentioned data sharing methods based on privacy protection technology.

[0021] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements any of the above-mentioned data sharing methods based on privacy protection technology.

[0022] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below: Figure 1 This is one of the structural schematic diagrams of a data sharing system based on privacy protection technology according to an embodiment of the present invention; Figure 2 This is a second schematic diagram of the structure of a data sharing system based on privacy protection technology according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the workflow of a data sharing system based on privacy protection technology according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating a data sharing method based on privacy protection technology according to an embodiment of the present invention. Detailed Implementation

[0024] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0025] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a data sharing system based on privacy protection technology, comprising: a data encryption module, a secure multi-party computation protocol module, a homomorphic encryption computation module, and a user interface module; The data encryption module is used to: encrypt the input data of at least one participant using an encryption algorithm to obtain encrypted data.

[0027] The data encryption module includes a data receiving unit, an algorithm selection unit, an encryption processing unit, and a data output unit. The specific implementation process for obtaining encrypted data is as follows: 1) The data receiving unit is responsible for receiving input data from at least one participant. This input data is cleaned, standardized, de-identified, and de-identified by the system's data preprocessing module before being transmitted to the data encryption module. The data receiving unit needs to have high throughput and reliable data verification capabilities to ensure that the received data is complete and conforms to the initial requirements of the encryption process, laying the foundation for subsequent encryption operations.

[0028] 2) After the data receiving unit confirms that the data is ready, the algorithm selection unit is activated. This unit has a built-in encryption algorithm library containing various verified encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm and the RSA algorithm. Based on preset security policies, the sensitivity level of the input data, and subsequent computational requirements, the algorithm selection unit automatically or manually selects one or a set of encryption algorithms. For example, if subsequent processes require homomorphic encryption computation, the algorithm selection unit will prioritize encryption algorithms that support homomorphic properties. The output of this unit is the specific encryption algorithm and its parameters to be used for this encryption process.

[0029] 3) The encryption processing unit is the component that performs the core encryption operations. It obtains the specified encryption algorithm and parameters from the algorithm selection unit and the input data to be processed from the data receiving unit. The encryption processing unit calls the corresponding encryption algorithm function, taking the input data from the participants as plaintext input, and combines it with the generated or specified encryption key to perform complex mathematical transformations. This process transforms the original, readable input data into an unreadable, seemingly random ciphertext sequence, i.e., encrypted data. The encryption processing unit ensures that the entire computation process is carried out in a secure and isolated environment, preventing the leakage of keys and intermediate process data.

[0030] 4) The encrypted data generated by the encryption processing unit is transmitted to the data output unit. This unit is responsible for formatting and encapsulating the encrypted data, attaching necessary metadata such as the encryption algorithm identifier, data source participant identifiers, and timestamps. Subsequently, the data output unit distributes the complete encrypted data packet to the subsequent secure multi-party computation protocol module or homomorphic encryption computation module according to the system's internal communication protocol for further secure computation. The data output unit also maintains a log of the encrypted data distribution to ensure the process is auditable.

[0031] The input data from participating parties refers to a collection of data provided by any entity accessing this data-sharing system and intended for shared computation. This data can originate from diverse sources, such as enterprise operational logs, sensor readings from IoT devices, anonymized personal behavioral records, or statistical information from public institutions. Before entering the data encryption module, this data has undergone processing by the data preprocessing module to remove obvious errors and invalid values, standardize formats and standards, and remove identifying information that can directly or indirectly identify a specific individual or entity. The input data from participating parties constitutes the raw material for the platform's value mining and collaborative computation; its privacy and security are the starting point for the entire system's protection.

[0032] The encryption algorithm can be either symmetric or asymmetric. Symmetric encryption algorithms use the same key for both encryption and decryption, resulting in faster computation speeds. Asymmetric encryption algorithms use a mathematically linked pair of public and private keys, used for encryption and decryption respectively, enhancing the flexibility of key management.

[0033] The secure multi-party computation protocol module is used to: receive encrypted data from multiple participants and perform secure multi-party computation without decryption to obtain the joint computation result; The secure multi-party computation protocol module includes a protocol initialization unit, a data coordination and verification unit, a encrypted collaborative computation unit, and a result aggregation and output unit. The specific implementation process for obtaining the joint computation result is as follows: 1) When it is determined that a joint computation task involving multiple parties needs to be executed, the protocol initialization unit is triggered. This unit is responsible for establishing the basic environment for this secure multi-party computation session. The protocol initialization unit first establishes secure communication channels with all relevant parties, which are typically encrypted using transport layer security protocols. Next, based on the type of computation task, such as privacy set intersection, secure summation, or secure mean calculation, the unit selects and loads the corresponding secure multi-party computation protocol logic from the built-in protocol library. Simultaneously, the unit generates or distributes computational parameters required for this session, such as asymmetric key pairs, homomorphically encrypted public parameters, or random number seeds for secret sharing, and securely distributes these parameters to each participant and other units within the module, preparing for subsequent collaborative computation.

[0034] 2) After protocol initialization, the data coordination and verification unit begins operation. This unit is responsible for receiving encrypted data from multiple participants. This encrypted data is generated by processing the original data from each participant through the data encryption module. The primary task of the data coordination and verification unit is to coordinate and align all received encrypted data, ensuring that they correspond to the same computational task and the same batch processing identifier. The unit verifies the metadata accompanying each piece of encrypted data, including the data source participant identifier, the encryption algorithm identifier used, and the data timestamp, to confirm the validity and freshness of the data. For encrypted data with incorrect format or failed verification, the unit requests the corresponding participant to resend it or excludes it from the current computational task, ensuring the consistency and reliability of the input dataset.

[0035] 3) Once the input data is ready and verified, the ciphertext collaborative computation unit, as the core computation component, is activated. This unit strictly follows the specific protocol logic for secure multi-party computation loaded during the protocol initialization phase. The unit's design allows it to perform prescribed computations on encrypted data without decrypting any of the participants' encrypted data throughout the process. The computation process is completed through multiple rounds of message exchange and local computation. For example, in secure multi-party computation using a secret-sharing scheme, the unit instructs each participant to perform addition or multiplication operations on their local encrypted data shares; in secure multi-party computation based on homomorphic encryption, the unit directly performs ciphertext addition and multiplication operations on the received homomorphically encrypted data. All intermediate computation results remain encrypted or secretly shared within the unit and during network transmission, ensuring that no single participant or other observer can deduce the original encrypted data content of other participants from the intermediate information.

[0036] 4) After the encrypted collaborative computation unit completes all preset rounds of computation, the result aggregation and output unit begins executing the final stage task. This unit collects the final output fragments from each participant or generated during the encrypted collaborative computation process. These output fragments may still be encrypted or distributed. The result aggregation and output unit performs an aggregation operation on these fragments according to the specifications of the secure multi-party computation protocol. This aggregation operation may be a simple combination or may require a final decryption or reconstruction. This decryption or reconstruction only reveals the final computation result and does not expose the encrypted input data of any individual participant. Finally, the unit generates a clear joint computation result. The result aggregation and output unit formats this joint computation result, appends the metadata of this computation task, and outputs it. Depending on the system architecture design, this joint computation result can be sent to the blockchain integration module for notarization or returned to authorized participants through the user interface module.

[0037] The joint computation result is the final output generated by the secure multi-party computation protocol module after completing a full secure multi-party computation task. This result is the product of collaborative computation on the encrypted data provided by all participants according to a predetermined computation function. For example, this result might be the sum or average of sales data from multiple participants, or a list of shared user IDs obtained after intersection of privacy sets. The joint computation result itself is a business-meaning numerical value or data set, revealing aggregated or correlated information from the multi-party data, but it absolutely does not contain any specific content that could be used to deduce or disclose the original private input data of any individual participant. The joint computation result embodies the value of multi-party collaborative data and is the core product delivered by the secure multi-party computation protocol module to the system or user.

[0038] The homomorphic encryption computation module is used to: perform computation operations on encrypted data from a single participant in an encrypted state to obtain the encrypted computation result; The homomorphic encryption computation module includes a parameter management unit, a computation scheduling unit, a ciphertext computation unit, and a result processing unit. The specific implementation process for obtaining the encryption computation result is as follows: 1) When a single participant generates encrypted data through the data encryption module and needs to perform computations on it, the parameter management unit is activated first. This unit is responsible for managing all homomorphic cryptographic parameters related to this computation task. The parameter management unit loads public parameters from the system security policy library or the participant-specified configuration that perfectly match the homomorphic encryption scheme used for the input encrypted data. These public parameters define the mathematical structural foundation upon which the homomorphic encryption operation relies, such as the dimension and modulus of the polynomial ring. Simultaneously, the unit ensures that the secret key used for potential subsequent decryption is securely stored in a hardware security module or a specific key management service, without directly interacting with the computation process. The parameter management unit provides a correct and consistent cryptographic context for the entire module's computation.

[0039] 2) Once the cryptographic environment is ready, the computation scheduling unit begins its work. This unit receives computation task instructions from the user interface module or upstream modules. The instructions explicitly describe the computational operations to be performed on the encrypted data, such as summing or averaging several encrypted data items, or executing a simple polynomial function containing combinations of addition and multiplication. The computation scheduling unit parses the instructions, decomposing them into an ordered sequence of computational operations composed of basic homomorphic operations. The unit verifies whether each operation in this sequence is supported by the current homomorphic encryption scheme; for example, some homomorphic encryption schemes may only support an unlimited number of additions or a finite number of multiplications. After successful verification, the computation scheduling unit transmits this sequence of computational operations, along with the identifier of the encrypted data to be processed, to the ciphertext computation unit and monitors the execution status of the entire computation job.

[0040] 3) The ciphertext computation unit is the core computing component of the homomorphic encryption computation module. It obtains a defined sequence of computational operations from the computation scheduling unit and extracts one or more corresponding encrypted data as input from the system's data bus. This encrypted data originates from a single participant, but may consist of multiple data submissions from that participant at different times. Based on the mathematical definition of the homomorphic encryption scheme, the ciphertext computation unit directly performs computational operations on the encrypted data. For addition operations, the unit performs a specific ciphertext addition operation on the two encrypted data inputs; for multiplication operations, it performs ciphertext multiplication. All operations are completed in an encrypted state, and the resulting data remains encrypted. If the sequence of computational operations is complex, the unit processes them sequentially, using the encrypted output of the previous step as the encrypted input of the next step, gradually obtaining intermediate or final results in the final encrypted form. The entire process does not require calling the decryption function, and the content of the original encrypted data remains protected throughout.

[0041] 4) After the ciphertext computation unit completes all operations specified in the computational operation sequence, the result processing unit takes over the subsequent work. This unit receives the final output from the ciphertext computation unit, which is still encrypted data, i.e., the encrypted computation result. The result processing unit performs standardized encapsulation on this encrypted computation result. The encapsulation includes attaching necessary metadata to the encrypted computation result, such as the generation time, a summary of the executed computational operation sequence, the corresponding original task instruction ID, and the version of the homomorphic encryption scheme used. Subsequently, the result processing unit outputs the encapsulated encrypted computation result to the specified downstream module according to the system configuration. For example, the encrypted computation result can be sent to the blockchain integration module for notarization, or it can be directly returned to the authorized user who made the computation request through the user interface module. For authorized users, they need to use the corresponding secret key to decrypt the encrypted computation result locally or in a trusted environment to obtain a meaningful plaintext result.

[0042] The encrypted computation result is the final output data generated after the homomorphic encryption computation module executes the specified sequence of computation operations. It is the new encrypted data obtained by performing a series of homomorphic operations on one or more original encrypted data from a single participant in an encrypted state. The encrypted computation result itself is still ciphertext, but it implicitly contains the plaintext result that should be obtained after performing the same sequential computation on the original plaintext data. Only an authorized party with the correct decryption key can decrypt the encrypted computation result and restore the final plaintext value or information with business significance. The generation of the encrypted computation result allows data to be processed and utilized with complete confidentiality, achieving a balance between privacy protection and data value mining.

[0043] The user interface module is used to provide data input and calculation result output functions.

[0044] The user interface module includes a user authentication and permission verification unit, a data submission and encapsulation unit, a task management and query unit, and a result rendering and delivery unit. Therefore: 1) Any user interaction with the system must first pass through the user authentication and permission verification unit. This unit provides a secure login interface and receives user credentials, such as a username and password combination, digital certificate, or biometric information. The unit compares and verifies the credentials against a securely stored user identity database. Upon successful verification, the unit loads the user's corresponding list of operation permissions based on the user's preset role and permission policy. In subsequent user sessions, this unit continuously acts as a gatekeeper, performing real-time permission verification on every data input request or calculation result output request initiated by the user, ensuring that the user can only perform operations within their authorized scope and preventing any unauthorized access.

[0045] 2) When an authorized user needs to input data, the data submission and packaging unit begins operation. This unit provides the user with an intuitive data submission interface that supports various data upload methods, including file upload, form filling, or application programming interface (API) calls. The user selects or enters the raw data to be shared on the interface and fills in the necessary metadata description, such as data type, sensitivity level, and intended use. After receiving this raw data and metadata, the data submission and packaging unit first performs preliminary structured validation according to system specifications. Next, the unit packages the raw data and metadata into a standardized data submission request object. Finally, the unit transmits this request object completely to the system's data preprocessing module through an internal secure communication link, thereby initiating the subsequent data processing flow.

[0046] 3) The task management and query unit is responsible for handling the output of computation results and related status tracking. This unit provides users with an interactive dashboard or task list interface. Users can initiate new computation task requests through this interface, such as specifying the execution of homomorphic encryption computation on certain uploaded encrypted data, or initiating a secure multi-party computation task involving multi-party data. The task management and query unit receives the user's request, generates a unique task tracking identifier, and returns it to the user. Simultaneously, the unit dispatches the task request to the corresponding computation module within the system. Furthermore, this unit continuously polls for or receives task status updates from peer modules such as the blockchain integration module and the secure multi-party computation protocol module. Users can query the current execution status of computation tasks at any time through the task tracking identifier in the interface, such as "queuing," "computing," or "completed."

[0047] 4) When a computation task is completed and the user has the corresponding access permissions, the result rendering and delivery unit is triggered. This unit retrieves the final computation result from the output buffer of the blockchain integration module or the computation module. The computation result may be a federated computation result or a cryptographic computation result. The unit adaptively renders the result based on its type and the user's device characteristics. For cryptographic computation results that can be decrypted, the unit prompts the user to decrypt and view them through a secure local client. For other types of structured results, the unit converts them into a user-friendly display format, such as charts, reports, or downloadable data files. Finally, the unit pushes the rendered result content or result file link to the user interface module's interface for the user to view and download via a secure network channel, and records the delivery log.

[0048] The data input function is one of the fundamental capabilities provided by the user interface module. It enables participating users to securely submit their local raw data to the data sharing system. This function is not merely an entry point for uploading files; it encompasses a complete interactive process, including guiding users to select data, performing front-end validation of data formats, receiving user-defined attribute annotations, and reliably transmitting data and descriptive information to the system's internal data preprocessing pipeline. The design goal of the data input function is to reduce the complexity of user operations while ensuring the quality and standardization of input data, preparing for subsequent encryption and computation processes.

[0049] The computation result output function is another fundamental capability of the user interface module. It is responsible for securely, accurately, and promptly presenting various computation results generated internally by the system to authorized users. The computation results may originate from joint computations generated by the secure multi-party computation protocol module or from encrypted computations generated by the homomorphic encryption computation module. The computation result output function includes not only simple result transmission but also real-time notification of result status, formatted display of result content, and integrity verification and secure download support for result files. This function is a crucial link in realizing the closed loop of data sharing value, ensuring that users can easily access and utilize the insights generated by collaborative computation.

[0050] Optionally, the above technical solution also includes a blockchain integration module, which is used to: record the joint calculation results and encrypted calculation results in the form of transactions in a distributed ledger, and verify the integrity and source of the calculation results based on the blockchain consensus mechanism.

[0051] The blockchain integration module includes a transaction construction unit, a consensus participation unit, a ledger storage and synchronization unit, and a query and verification unit. Therefore: 1) When the secure multi-party computation protocol module generates a joint computation result or the homomorphic cryptographic computation module generates an encrypted computation result, these results are sent to the blockchain integration module. The transaction construction unit is responsible for receiving these computation results. The unit creates a standard blockchain transaction data structure for each computation result. This transaction data structure contains several key parts: the transaction header records a timestamp and transaction type identifier; the transaction payload stores the hash value of the computation result data or the encrypted result data itself; the additional information section records the computation metadata, such as the task identifiers of the participants, a list of participant identifiers, the version information of the computation module used, and references to previous related transactions. The transaction construction unit uses its own asymmetric key to digitally sign the transaction data, ensuring the non-repudiation and authenticity of the transaction source. Finally, the unit outputs the constructed transaction data to be confirmed to the next unit.

[0052] 2) The consensus participation unit receives transactions awaiting confirmation from the transaction construction unit. This unit, representing the blockchain integration module, connects to the pre-defined blockchain network as a node. The unit broadcasts the transactions awaiting confirmation to other peer nodes throughout the blockchain network. Simultaneously, the consensus participation unit continuously listens for transactions and blocks broadcast by other nodes in the network. The unit incorporates client logic for blockchain consensus algorithms, such as node logic using Byzantine fault tolerance or proof-of-stake algorithms. Depending on the adopted blockchain consensus mechanism, the consensus participation unit participates in the proposal, verification, and voting processes for new blocks. For newly emerging candidate blocks in the network containing transactions of interest to this module, the consensus participation unit rigorously verifies the correctness of all transaction formats, the validity of digital signatures, and the logical consistency between transactions within the block. Only blocks that pass verification are accepted by the unit.

[0053] 3) Once a transaction block containing specific computational results is officially confirmed through consensus within the blockchain network, the ledger storage and synchronization unit begins its work. This unit is responsible for appending the newly confirmed block to its local distributed ledger copy. The distributed ledger is stored in the nodes' persistent storage devices as a blockchain linked chronologically. The unit also maintains a state database for efficiently indexing key information in transactions, such as quickly finding relevant transaction records by task identifier or participant identifier. Furthermore, the ledger storage and synchronization unit continuously communicates with other peer nodes in the network, comparing block heights and block hashes to ensure that its local distributed ledger copy is consistent with the majority of nodes across the network. If a local ledger version is found to be outdated, the unit automatically synchronizes missing blocks from other nodes, completing the ledger data repair and synchronization, ensuring data integrity and consistency.

[0054] 4) The query and verification unit is the interface unit through which the blockchain integration module provides services. When the user interface module or other system modules need to query the storage information of a computation result or verify its integrity, the query request is sent to the query and verification unit. Based on the task identifier or transaction hash in the request, this unit quickly searches the locally maintained state database and distributed ledger to locate the specific transaction that recorded the computation result and its corresponding block. The unit can extract the hash value of the computation result stored in the transaction and perform a real-time hash operation comparison with the computation result data provided by the requester to verify whether the data has been tampered with since its recording. Simultaneously, the unit can provide verifiable proof information such as how many subsequent blocks have confirmed the transaction, at which block height it was recorded, and the block generation time, thereby confirming the source and integrity of the computation result.

[0055] Distributed ledger is a database recording technology that shares, replicates, and synchronizes data across multiple nodes or sites in a network. In the context of blockchain integration modules, a distributed ledger specifically refers to the data storage structure used to sequentially record all computational results and transactions. It is not maintained by any single central authority, but rather each node connected to the blockchain network independently maintains a complete or partial copy of the data. The data in the ledger is organized into a series of chronologically linked data blocks, each containing a batch of verified transaction records. Any tampering with historical transaction records will cause the verification of all subsequent data blocks to fail, thus being rejected by other nodes in the network. Distributed ledgers provide a persistent, transparent, and immutable storage foundation for computational results.

[0056] The blockchain consensus mechanism is a set of predefined rules and algorithms used to ensure that all nodes in a distributed network reach a consensus on the content of a new block and the current state of the ledger without a central coordinator. The consensus participation units of the blockchain integration module operate according to these rules. Common blockchain consensus mechanisms include Proof-of-Work, Proof-of-Stake, and Practical Byzantine Fault Tolerance. These mechanisms ensure that honest nodes in the network can work collaboratively through economic incentives, staked assets, or voting, effectively processing transaction requests submitted by the transaction construction unit, packaging valid transactions into new blocks, and ultimately determining which blocks are legitimate and should be added to the distributed ledger. The blockchain consensus mechanism is a crucial guarantee for ensuring the reliability of transaction records and preventing double-spending and malicious behavior.

[0057] Optionally, the above technical solution also includes an access control module, which is used to: perform fine-grained access control on the calculation results stored in the blockchain integration module according to preset permission rules.

[0058] The access control module includes a permission rule management unit, an access request interception and parsing unit, a policy decision-making unit, and an access execution and auditing unit. Therefore: 1) The Permission Rule Management Unit is the fundamental configuration center of the Access Control Module. This unit provides a management interface or application programming interface (API) that allows system administrators to define and maintain fine-grained access control policies. These policies are stored in the form of rules, each clearly describing under what conditions which subject can perform what operation on which object. Subjects typically refer to users, user groups, or roles; objects specifically refer to the joint computation results or cryptographic computation results stored in the blockchain integration module, which can be located using unique identifiers such as transaction hashes and task identifiers; operations include reading, downloading, or verifying. Rule conditions can be very granular, such as restricting access time ranges, setting access frequency limits, or requiring additional two-factor authentication. The Permission Rule Management Unit compiles these rules and stores them in a high-speed policy library for real-time querying by subsequent units.

[0059] 2) When a user attempts to access a computation result through the user interface module or other means, the access request interception and parsing unit begins operation. This unit is deployed at key nodes in the access path, listening for all requests directed to data in the blockchain integration module. Once an access request is captured, the unit immediately intercepts it. Next, the unit parses the original request, extracting the crucial security token, the identifier of the requested computation result, and the type of operation requested. The unit verifies the validity of the security token to confirm the identity of the requesting entity. Finally, the unit packages the parsed structured information, including the subject identifier, object identifier, and operation intent, into a standardized policy check request and passes it to the next unit for processing.

[0060] 3) The policy decision unit receives standardized policy check requests from the access request interception and parsing unit. This unit, acting as the "brain" of the access control module, is responsible for making the final permission or denial decision. The policy decision unit first queries the policy library maintained by the permission rule management unit, retrieving all permission rules related to the subject and object in the current request. Then, the unit matches and logically evaluates the request's context information against the conditions of each rule. This process may involve calculating attributes, checking environment status, or calling external authorization services. If one or more rules explicitly allow the request, and no higher-priority rule prohibits it, the policy decision unit generates a "allow" decision; otherwise, it generates a "deny" decision. This decision, along with the complete context of the request, is immediately output.

[0061] 4) The Access Execution and Audit Unit receives the output decision from the Policy Decision Unit. If the decision is "Allow," the unit processes the previously intercepted original access request as necessary, such as by attaching a temporary access token, and then allows it to proceed to the query and verification unit of the blockchain integration module to obtain data. If the decision is "Deny," the unit directly returns an access denial error message to the request initiator and terminates the subsequent process of the request. Regardless of the decision outcome, the Access Execution and Audit Unit records the detailed information of this access request, the decision result, and the timestamp as an immutable audit log in the system's dedicated audit trail database, ensuring that all access behaviors are traceable and auditable afterward.

[0062] Fine-grained access control is a precise permission management method that allows system administrators to define and enforce access rules based on a series of detailed attributes, conditions, and contexts, rather than just coarse-grained identities or roles. In the context of the access control module, fine-grained access control manifests in its ability to specify different operational permissions for each user subject under different environmental conditions, for each specific computational result object stored in the blockchain integration module. For example, it can stipulate that user A can only view the final result summary of task X during weekly working hours, but cannot download the original encrypted computation result file; while user B can download the file, but not more than three times a day. This granular control capability greatly reduces the risk of data misuse or unauthorized access, and is a necessary guarantee for achieving secure data sharing.

[0063] Optionally, the above technical solution also includes a data preprocessing module, which is used to: clean, standardize, de-identify and de-identify all input data, and provide the processed data to the data encryption module for encryption to obtain encrypted data.

[0064] The data preprocessing module includes a data receiving and verification unit, a data cleaning unit, a data standardization unit, a data de-identification and de-identification unit, and a data output unit. Therefore: 1) The data receiving and verification unit is the entry point for the data preprocessing module. This unit receives raw input data submitted by participants from the user interface module via a secure application programming interface or message queue. The unit performs initial verification on the received data packets, checking whether the packet format conforms to the protocol specifications, confirming whether the data integrity checksum matches, and verifying whether basic metadata fields such as data size and type are within acceptable ranges. For data packets that fail verification, the unit generates an error report and sends it back to the source, requesting retransmission. For data that passes verification, the unit unpacks it, temporarily stores the raw input data and its metadata in a protected buffer, and triggers a data processing task event, marking the formal start of the preprocessing process.

[0065] 2) The data cleaning unit reads raw input data from the buffer. Based on predefined business rules and quality standards, this unit performs a series of automated cleaning operations. These operations include identifying and handling missing values, such as filling them with the column mean, median, or specific indicators; detecting and correcting obvious outliers and errors, such as values ​​outside the reasonable range or strings that do not conform to the format; finding and merging or deleting identical duplicate records; and performing basic syntax corrections, such as standardizing date formats and correcting spelling errors. The data cleaning unit typically uses a configurable cleaning rule library, allowing administrators to customize cleaning logic for different types of data sources. The cleaning process generates a data quality report, recording all modified data items and their original values.

[0066] 3) After data cleaning, the data standardization unit begins processing the data. The goal of this unit is to transform data from different participants, which may have different structures and representations, into a standardized format uniform within the system. The data standardization unit performs structure mapping, matching and mapping the field names of the input data with the attributes in the system's standard data model. The unit performs data type conversion to ensure that all similar data, such as integers, floating-point numbers, and text, use the same internal storage format. The unit also standardizes units of measurement and encoding standards; for example, unifying all currency amounts to RMB and converting all country and region names to standard ISO codes. After processing by the data standardization unit, the data achieves consistency in both structure and semantics, laying the foundation for subsequent collaborative computing.

[0067] 4) The data anonymization and de-identification unit is a crucial step in privacy protection. This unit applies anonymization rules to the standardized data. Anonymization operations include masking, replacing, or generalizing sensitive fields, such as replacing the middle digits of a credit card number with asterisks, or replacing a precise age with an age range. Next, the unit performs de-identification processing, using technical means to remove or alter identifiers in the data that can directly or indirectly identify a specific natural person. This includes deleting direct identifiers such as names, ID numbers, and mobile phone numbers; generalizing or suppressing quasi-identifiers such as postal codes, dates of birth, and gender; and potentially applying algorithms such as k-anonymity or differential privacy to ensure that the processed data cannot be associated with a specific individual. The unit ensures that all operations comply with privacy regulations and records the anonymization and de-identification strategies applied.

[0068] 5) The data output unit is responsible for the final stage of the preprocessing process. This unit receives data from the data anonymization and de-identification unit, which has undergone all preprocessing operations. The data output unit performs final encapsulation and formatting of this processed data. During encapsulation, the unit binds the processed data together with the data quality report generated in previous steps, the applied standardized mapping rules, and the de-identification strategy summary to form a complete data preprocessing record package. Subsequently, the data output unit reliably transmits the core processed data portion of this record package to the data encryption module through an internal secure data transmission channel. This data serves as input data for encryption, resulting in encrypted data. The data output unit also archives and stores the preprocessing record package for future auditing and traceability.

[0069] This invention discloses a data sharing system based on privacy protection technology, providing innovative solutions for fields with stringent requirements for data security and privacy protection, such as finance, healthcare, and legal services. The following detailed explanation uses the specific scenario of "cross-institutional credit risk assessment and anti-fraud joint analysis" in the financial industry as an example. This scenario involves multiple commercial banks jointly constructing more accurate customer risk profiles and fraud identification models without disclosing their respective customers' sensitive credit data. The entire process seamlessly connects all modules and internal units of the system, demonstrating a logically rigorous and technically feasible collaborative operation process from data input to result output. The specific implementation process is as follows: 1) Multiple partner banks, as participants, decided to share some anonymized customer behavior data and transaction records to jointly analyze potential credit risks and fraud patterns. Each bank's business system uploads the raw dataset through the data submission and packaging unit of the user interface module. After preliminary verification of the file format, the data submission and packaging unit transmits the data to the data preprocessing module. The data receiving and verification unit of the data preprocessing module receives this raw input data and verifies the integrity and legality of the data packet source. Subsequently, the data cleaning unit begins operation, automatically correcting or removing missing values, abnormal transaction amount records, and incorrectly formatted fields in the data. The data standardization unit then works to uniformly map the different data field names of each bank to the attributes defined in the "Customer Risk Analysis Standard Model," and standardize the monetary units, date formats, and classification codes. Next, the data anonymization and de-identification unit performs key privacy protection operations, replacing direct identifiers such as customer names and ID numbers with pseudonyms, generalizing quasi-identifiers such as precise addresses and workplaces, and possibly applying algorithms to ensure that the data meets k-anonymity requirements. Finally, the data output unit securely transmits the cleaned, standardized, de-identified, and de-identified data to the data encryption module.

[0070] 2) The data receiving unit of the data encryption module receives the processed data from the data preprocessing module. The encryption algorithm selection unit selects a suite of encryption algorithms from the algorithm library that supports homomorphic encryption and secure multi-party computation, based on the requirements of the collaborative computation task. The encryption processing unit calls the selected encryption algorithm and uses the encryption key generated for this collaborative task to encrypt the processed data from each bank, transforming the plaintext data into encrypted data that cannot be directly deciphered. The data output unit attaches metadata tags to each piece of encrypted data and then transmits it in two paths: one copy is sent to the secure multi-party computation protocol module for collaborative computation, and the other copy is sent to the homomorphic encryption computation module for possible unilateral in-depth analysis.

[0071] 3) Depending on the analysis objectives, the system launches different computation modules in parallel or sequentially. For tasks requiring aggregated statistics, the secure multi-party computation protocol module is activated. This module's protocol initialization unit establishes a secure channel with all participating banks and loads the specific secure multi-party computation protocol. The data coordination and verification unit receives encrypted data from multiple banks and verifies its consistency and validity. The encrypted collaborative computation unit, without decryption, strictly follows the protocol logic to perform multiple rounds of secure interactive computation on the encrypted data, such as jointly calculating the distribution of customer delinquency rates across institutions or identifying groups with shared debt risks. The result aggregation and output unit ultimately generates and outputs a joint computation result, such as an encrypted risk index matrix. For banks that wish to independently train models using their own data, the homomorphic encrypted computation module is invoked. This module's parameter management unit loads homomorphic encrypted parameters, the computation scheduling unit parses the model training instructions into a series of homomorphic addition and multiplication operation sequences, and the encrypted computation unit performs these computation operations on the encrypted data from that single participant in an encrypted state. Finally, the result processing unit outputs an encrypted computation result, i.e., an encrypted machine learning model parameter file.

[0072] 4) Both joint and encrypted computation results enter the blockchain integration module. The transaction construction unit of this module creates a blockchain transaction for each computation result. This transaction includes key information such as the hash digest of the computation result, task identifier, and list of participants, and is digitally signed. The consensus participation unit broadcasts this transaction to the blockchain network and participates in the network-wide consensus process, verifying and confirming a new block containing this transaction. The ledger storage and synchronization unit appends the confirmed new block to its local distributed ledger copy, achieving a permanent and immutable record of the computation result. The query verification unit is ready to respond to external requests for verification of the transaction's existence and data integrity.

[0073] 5) The access control module's permission rule management unit is pre-configured with complex access control policies by the consortium administrator. For example, the policy stipulates that only the risk directors of Bank A and Bank B, who participated in the calculation, can view the joint calculation result of the "Cross-Institutional Joint Debt Risk Report"; while the encrypted calculation result of the "Fraud Identification Model" trained independently by Bank C can only be downloaded by specific users in Bank C's internal audit department, and the download operation must be performed during office hours. When a user initiates an access request, the access request interception and parsing unit intercepts the request, parses the user's identity, the requested calculation result object, and the operation type. The policy decision unit performs real-time reasoning and matching based on the policies in the permission rule management unit, making a decision to allow or deny. The access execution and auditing unit executes this decision, allowing legitimate requests or rejecting illegitimate requests, and records the complete log of this access in the audit database.

[0074] 6) Authorized users interact with the system through the user interface module. The user authentication and permission verification unit verifies the user's identity upon login. Users submit new computation tasks or query historical task statuses through the task management and query unit. When users need results, the result rendering and delivery unit begins operation. For decryptable joint computation result summary reports, this unit renders and presents them directly on the user interface in the form of visual charts. For encrypted computation result files that require confidentiality, such as model parameters, the result rendering and delivery unit provides a secure download link for the encrypted files and prompts the user to decrypt them using a local key. Throughout the process, the user interface module provides a closed-loop interactive experience from data input to computation result output.

[0075] Through the detailed examples in the field of financial credit risk control above, this invention fully demonstrates how the data preprocessing module, data encryption module, secure multi-party computation protocol module, homomorphic encryption computation module, blockchain integration module, access control module, and user interface module, along with their respective internal units, organically collaborate to construct a technical system that achieves secure, efficient, and reliable sharing of data elements while strictly protecting data privacy. This logical framework can also be seamlessly adapted to patient data privacy protection analysis in multi-center clinical studies in the medical field, or to the compliance collaborative review of electronic evidence across law firms in legal services.

[0076] like Figure 2 As shown, the system of the present invention includes a data encryption module and a secure multi-party computation protocol module (MPC). Figure 2 The system includes a Secure Multi-Party Computation (SMC) protocol module, a homomorphic encrypted computation module, a blockchain integration module, and an access control module. In addition, the system also includes a user interface module and a data preprocessing module to support the complete business process. The user interface module provides data input and computation result output functions. The data preprocessing module is responsible for cleaning, standardizing, de-identifying, and de-identifying all input data before encryption. Specifically: 1) Data input source: In Figure 2 At the top, labeled "Data Input," multiple arrows connect to the "Data Preprocessing Module" and subsequently the "Data Encryption Module," indicating that the data sources are diverse, such as monitoring and sensors, social media, and public administration data. This data is submitted by at least one participant through the user interface module and undergoes initial processing by the data preprocessing module.

[0077] 2) Data encryption module: Receives the processed data from the data preprocessing module and uses an encryption algorithm to encrypt the input data of at least one participant to obtain encrypted data.

[0078] 3) The secure multi-party computation protocol module is used to receive encrypted data from multiple participants and perform secure multi-party computation without decryption to obtain the joint computation result.

[0079] 4) The homomorphic encryption computation module primarily performs computational operations on encrypted data from a single participant under encrypted conditions to obtain the encrypted computation result. This module can operate in parallel or independently of the secure multi-party computation protocol module.

[0080] 5) The blockchain integration module is responsible for recording the joint computation results and encrypted computation results in the form of transactions in the distributed ledger, and verifying and storing the integrity and source of the computation results based on the blockchain consensus mechanism.

[0081] 6) The access control module performs fine-grained access control on the computation results stored in the blockchain integration module according to preset permission rules, and manages users' access permissions to the computation results.

[0082] like Figure 3 As shown, the data processing procedure of the system of the present invention is described as follows, specifically including: 1) Data Input and Preprocessing: Specifically, the data input layer is the starting point of the entire process. Participants submit data to the platform, an action completed through the data collection interface of the user interface module. The data submission and encapsulation unit of the user interface module provides efficient and secure data reception capabilities. Simultaneously, this unit performs preliminary data cleaning and verification to ensure that the received data conforms to the platform's basic format and integrity requirements. Subsequently, the data enters the data preprocessing layer. In this layer, data cleaning and verification are carried out in depth by the data cleaning unit of the data preprocessing module, including removing invalid or erroneous data records. The data standardization module, i.e., the data standardization unit of the data preprocessing module, is responsible for converting data from different sources into a unified format and structure within the system. The data desensitization and de-identification unit of the data preprocessing module further performs desensitization and de-identification processing on the data, making it impossible to directly associate personal data with a specific individual. The processed data is then transmitted to the next layer.

[0083] 2) Data Encryption and Privacy Protection: Specifically, the data then enters the data encryption and privacy protection layer. The core of this layer is the data encryption module. This module performs encryption processing, using encryption algorithms to encrypt the preprocessed input data from at least one participant, resulting in encrypted data. The data encryption module is designed to support multiple algorithms, adapting to different security levels and subsequent computation types, such as algorithms supporting homomorphic encryption.

[0084] 3) Secure Data Computation and Storage: Specifically, encrypted data then enters the secure computation layer for value extraction. This layer deploys two key computation modules in parallel: a secure multi-party computation protocol module and a homomorphic encrypted computation module. The secure multi-party computation protocol module coordinates multiple participants to perform secure multi-party computations on encrypted data from multiple parties without decryption, achieving collaborative analysis and obtaining joint computation results. The homomorphic encrypted computation module performs computation operations on encrypted data from a single participant in an encrypted state. This module supports basic operations on encrypted data, such as addition and multiplication, enabling complex statistical analysis to be completed with complete data confidentiality and outputting encrypted computation results. The computation results are then persisted and reliably stored in the blockchain layer. The blockchain integration module is the execution component of this layer, responsible for data storage and verification. The blockchain integration module records the joint computation results and encrypted computation results in the form of transactions in a distributed ledger and verifies the integrity and origin of the computation results based on the blockchain consensus mechanism. Through the data storage and verification functions of the blockchain layer, the immutability, transparency, and high reliability of the computation results are ensured.

[0085] 4) Data Access Control and Result Output: Specifically, the business process then enters the access control layer. This layer is implemented by the access control module, which performs fine-grained permission management on the computation results stored in the blockchain integration module according to preset permission rules. Fine-grained permission management ensures that only explicitly authorized users or systems can access specific computation results, thereby strictly protecting data privacy and security in the shared environment. Authorized results then enter the result output layer. In this layer, the result rendering and delivery unit of the user interface module is responsible for handling the final presentation and delivery of the results. This layer ensures the reliability of the result verification and sharing process: the authenticity of the results can be verified by the notarization stored in the blockchain integration module, while the sharing mechanism strictly follows the permission decisions of the access control module, securely delivering the results to authorized users in the form of reports, charts, or encrypted files.

[0086] 5) User Interface: Specifically, the user interface layer is the interaction entry point for the entire platform. This layer provides an intuitive user interface, integrating data submission and result access interfaces. Participants submit raw data through the data submission and encapsulation unit of the user interface module; various users initiate computation tasks and track their status through the task management and query unit of the user interface module, and obtain the final computation results through the result rendering and delivery unit. The user authentication and permission verification unit of the user interface module ensures the security and compliance of all interactive operations. The platform employs a secure transmission protocol throughout the entire data transmission process to ensure that data is not intercepted or tampered with during its flow.

[0087] Compared with the closest prior art, the technical advantages of this application are as follows: 1) Higher data processing efficiency and reduced latency caused by encryption and decryption: This proposal uses a homomorphic encryption computing module to perform computation operations directly on encrypted data in the encrypted state, and a secure multi-party computation protocol module to perform multi-party collaborative computation without decryption. This avoids the frequent decryption and re-encryption processes that must be performed for computation in traditional solutions, thereby reducing computation latency and improving overall data processing efficiency.

[0088] 2) Enhanced privacy protection capabilities, ensuring data security even during data sharing; this proposal constructs a multi-layered privacy protection system. The data encryption module uses encryption algorithms to encrypt input data; the secure multi-party computation protocol module ensures that the encrypted data of each participant is not leaked during multi-party collaborative computation; the homomorphic encryption computation module guarantees the encrypted state of a single participant's data during computation; the blockchain integration module provides a verifiable and tamper-proof record of the computation results; and the access control module implements fine-grained access control over the final results. These modules work together to ensure end-to-end privacy and security of data from input, computation to storage and access.

[0089] 3) Simplified management and operation processes reduce the complexity of technology implementation; this proposal simplifies the process through standardized module design. The data preprocessing module automates data cleaning, standardization, de-identification, and de-identification; the user interface module provides a unified interface for data submission and result access; the various functional modules are highly integrated and work collaboratively, encapsulating complex cryptographic operations and distributed ledger management within the platform, providing users with a smooth operating experience and reducing the complexity of deployment and maintenance of systems integrating secure multi-party computation, homomorphic encryption, and blockchain technologies.

[0090] The commercial value of this application lies in its innovation and practicality. By providing a secure, efficient, and non-competitive data sharing platform, it solves some key problems existing in the current data sharing field, thereby bringing economic and social benefits to multiple industries. Specifically: 1) Data Security and Privacy Protection: In today's world, where data breaches and privacy violations are receiving increasing attention, this proposal constructs a comprehensive security and privacy protection system covering the entire data lifecycle through the synergy of data encryption modules, secure multi-party computation protocol modules, homomorphic encryption computation modules, blockchain integration modules, and access control modules. This ensures the security and privacy of data during the sharing process. This is particularly attractive to industries with extremely high data security requirements, such as finance, healthcare, and legal services.

[0091] 2) Improved operational efficiency: Through standardized data sharing processes, especially the automated processing of data preprocessing modules and the direct processing capabilities of various computing modules for encrypted data, enterprises can acquire and analyze data more quickly, thereby improving decision-making speed and operational efficiency. This efficiency improvement can directly translate into cost savings and faster market response.

[0092] 3) Fostering Innovation and Collaboration: This proposal's platform supports collaborative computing between different organizations and enterprises without disclosing raw data through a secure multi-party computation protocol module. This helps promote cross-industry collaboration and accelerate the development of new products and solutions. By sharing data, enterprises can better understand market demands and develop more innovative and competitive products.

[0093] 4) Enhance Market Competitiveness: By using the platform proposed in this proposal, enterprises can leverage internal and external data resources more securely and efficiently, thereby gaining a competitive edge in the market. Insights provided by the joint or encrypted computation results obtained from the platform can guide enterprises in developing more precise market strategies and increasing market share.

[0094] 5) Regulatory Compliance: With increasingly stringent data protection regulations, enterprises need to ensure that their data processing activities comply with relevant regulations. This proposal's platform provides a solution that meets these regulatory requirements. The data preprocessing module's anonymization and de-identification, the access control module's fine-grained permission management, and the blockchain integration module's auditable records work together to help enterprises meet compliance requirements when sharing data, avoiding legal risks and economic losses due to violations.

[0095] 6) Promoting social well-being: In the fields of smart cities and public safety, the platform proposed in this proposal can improve the efficiency of security data sharing and collaborative analysis among multiple departments, thereby improving the efficiency of emergency response and disaster management, protecting the lives and property of citizens, and having a positive impact on the overall well-being of society.

[0096] In summary, the commercial value of this application lies in providing enterprises with a secure, efficient, compliant, and adaptable data-sharing solution that helps them enhance competitiveness, promote innovation, and ensure social responsibility and regulatory compliance. With the continuous development of the data economy, the platform proposed in this application is expected to become an indispensable tool for enterprises and organizations.

[0097] "Non-rivalrous" means that data, as a factor of production, can be used simultaneously by multiple participants during the sharing process without affecting or reducing the availability or value of the same data for other parties due to the use of data by one party. This is in stark contrast to traditional data usage patterns (such as exclusive or monopolistic use).

[0098] Traditional technical solutions have limitations in data sharing, privacy protection, computing power, data security, access control, and technology integration, making it difficult to meet the high-efficiency, secure, and flexible requirements of modern data-intensive applications. Compared with traditional platforms, the non-competitive data element sharing platform based on privacy protection technology proposed in this proposal has the following specific improvements: 1) Data Sharing: Traditional platforms adopt a centralized model, which is limited by trust and security issues. The platform proposed in this paper supports decentralized multi-party collaboration through a secure multi-party computation protocol module and enhances the credibility of the sharing process by utilizing a blockchain integration module, thereby enhancing the security and privacy of data sharing.

[0099] 2) Privacy Protection: Traditional platforms have limited measures and are prone to data leakage. The platform proposed in this paper adopts advanced encryption technologies, such as secure multi-party computation and homomorphic encryption, to ensure data privacy throughout the computation process through secure multi-party computation protocol modules and homomorphic encryption computation modules.

[0100] 3) Computing power: Traditional platforms rely on local or central servers, which limits computing power. The platform proposed in this paper supports direct computation and multi-party collaborative computation on encrypted data, and unlocks the potential of distributed computing through homomorphic encrypted computation modules and secure multi-party computation protocol modules.

[0101] 4) Data Security: Traditional platforms rely on single security measures, making them vulnerable to attacks. This proposal introduces a blockchain integration module into the platform, utilizing blockchain technology to provide tamper-proof data storage and verification, thus constructing a multi-layered security system.

[0102] 5) Access Control: Traditional platform permission management is not flexible enough and lacks fine-grained control. The platform proposed in this paper achieves fine-grained permission management through an access control module, which can adapt to constantly changing access needs and accurately control access to calculation results.

[0103] 6) Technology Integration: Traditional platforms have low integration levels and may have compatibility issues. The platform proposed in this paper achieves a high degree of integration of data preprocessing module, data encryption module, secure multi-party computation protocol module, homomorphic encryption computation module, blockchain integration module, access control module, and user interface module. All components work together to provide a smooth user experience.

[0104] like Figure 4 As shown in the figure, a data sharing method based on privacy protection technology according to an embodiment of the present invention includes the following steps: S1. Use an encryption algorithm to encrypt the input data of at least one participant to obtain encrypted data; S2. Receive encrypted data from multiple participants and perform secure multi-party computation without decryption to obtain the joint computation result; S3. Perform computational operations on encrypted data from a single participant while in an encrypted state to obtain the encrypted computation result; S4. Receive data input from at least one participant through the data input function, and output joint calculation results and / or encrypted calculation results through the calculation result output function.

[0105] Optionally, the above technical solution also includes: recording the joint computation results and encrypted computation results in the form of transactions in a distributed ledger, and verifying the integrity and source of the computation results based on the blockchain consensus mechanism.

[0106] Optionally, the above technical solution also includes: performing fine-grained access control on the stored calculation results according to preset permission rules.

[0107] Optionally, in the above technical solution, before encrypting the input data of at least one participant using an encryption algorithm, the solution further includes: cleaning, standardizing, and de-identifying and de-identifying all input data.

[0108] It should be noted that the beneficial effects of the data sharing method based on privacy protection technology provided in the above embodiments are the same as those of the data sharing system based on privacy protection technology described above, and will not be repeated here. Furthermore, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation processes are detailed in the system embodiments, and will not be repeated here.

[0109] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned data sharing methods based on privacy protection technology.

[0110] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described data sharing methods based on privacy protection technology.

[0111] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0112] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A data sharing system based on privacy protection technology, characterized in that, It includes a data encryption module, a secure multi-party computation protocol module, a homomorphic encryption computation module, and a user interface module; The data encryption module is used to: encrypt the input data of at least one participant using an encryption algorithm to obtain encrypted data; The secure multi-party computation protocol module is used to: receive encrypted data from multiple participants, and perform secure multi-party computation without decryption to obtain a joint computation result; The homomorphic encryption calculation module is used to: perform calculation operations on the encrypted data from a single participant in an encrypted state to obtain an encryption calculation result; The user interface module is used to provide data input and calculation result output functions.

2. The data sharing system based on privacy protection technology according to claim 1, characterized in that, It also includes a blockchain integration module, which is used to: record the joint calculation results and the encrypted calculation results in the form of transactions in a distributed ledger, and verify the integrity and source of the calculation results based on the blockchain consensus mechanism.

3. A data sharing system based on privacy protection technology according to claim 2, characterized in that, It also includes an access control module, which is used to perform fine-grained access control on the calculation results stored in the blockchain integration module according to preset permission rules.

4. A data sharing system based on privacy protection technology according to any one of claims 1 to 3, characterized in that, It also includes a data preprocessing module, which is used to clean, standardize, de-identify and de-identify all input data, and provide the processed data to the data encryption module for encryption to obtain encrypted data.

5. A data sharing method based on privacy protection technology, characterized in that, include: The input data of at least one participant is encrypted using an encryption algorithm to obtain encrypted data. It receives encrypted data from multiple participants and performs secure multi-party computation without decryption to obtain the joint computation result; In an encrypted state, computational operations are performed on the encrypted data from a single participant to obtain an encrypted computation result; The system receives data input from at least one participant through the data input function and outputs joint calculation results and / or encrypted calculation results through the calculation result output function.

6. A data sharing method based on privacy protection technology according to claim 5, characterized in that, Also includes: The joint calculation results and the encrypted calculation results are recorded in a distributed ledger in the form of transactions, and the integrity and source of the calculation results are verified based on the blockchain consensus mechanism.

7. A data sharing method based on privacy protection technology according to claim 6, characterized in that, Also includes: Fine-grained access control is applied to the stored computation results based on preset permission rules.

8. A data sharing method based on privacy protection technology according to any one of claims 5 to 7, characterized in that, Before encrypting the input data of at least one participant using an encryption algorithm, the process also includes cleaning, standardizing, and de-identifying and de-identifying all input data.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a data sharing method based on privacy protection technology as described in any one of claims 5 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a data sharing method based on privacy protection technology as described in any one of claims 5 to 8.

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