A data sharing system, method, storage medium, and device

Abstract

This invention provides a data sharing system, method, storage medium, and device, belonging to the field of data protection technology. It combines the data of each node with a federated learning multi-party secure computation privacy architecture via a sidechain, and establishes a shared data model based on the sidechain's identity mechanism verification. In a sidechain-based data sharing privacy protection process, the data nodes on the sidechain are first authenticated to ensure their security. Then, during data sharing, data nodes can share data in three ways based on its importance: sharing, data modeling, and privacy protection. This ensures data security, preventing misuse and tampering, and also supports shared data modeling in public service and financial sectors, enabling a better data sharing loop. Due to the use of a sidechain-based data model sharing, both data utilization and data privacy protection security are significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of data protection technology, and specifically relates to a data sharing system, method, storage medium and device. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art or art.

[0003] Blockchain is a term in the field of information technology. Essentially, it's a shared database storing hash values ​​or information, characterized by being "unforgeable," "fully traceable," "transparent," and "collectively maintained." It's a novel application of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. It establishes sidechains on the blockchain, creating a consortium for data consensus and authentication. Combined with the privacy-preserving computation technology of federated learning, federated learning multi-party secure computation is a machine learning framework that effectively helps multiple institutions use data and perform machine learning modeling while meeting user privacy protection, data security, and government regulations. This application technology can be used in a wide range of everyday scenarios, including financial institutions, public services, and data authentication.

[0004] However, in practical applications, once the sidechain technology of a blockchain is certified in the consortium, the nodes of the sidechain can share the data on the chain, which makes the data of some important nodes easy to be tampered with. Moreover, the data privacy protection of the blockchain is limited, as all nodes can use the data on the chain, and it is impossible to protect the privacy of important data of nodes and build models.

[0005] In practical sidechain scenarios, because sidechains and federated learning privacy protection technologies are separate, they cannot achieve good efficiency in their respective domains, and they cannot effectively combine the data that nodes need to apply, mainly for the following reasons:

[0006] 1) All members on the blockchain need to be verified before they can enter the sidechain. Full verification is performed on the consortium blockchain to form a sidechain.

[0007] 2) On the sidechain, each node can share the data of the other node, and under certain mechanisms, the data of the node can be modified;

[0008] 3) For nodes on a sidechain, when different nodes need to use other data, they share it through the blockchain ledger. However, in some special scenarios, due to the importance of the data itself, they do not want to share the data and only want to give the data to another node for application.

[0009] 4) Compared to federated learning privacy technology, due to the lack of complete customization, the data in federated learning privacy technology is all processed to obtain a data model, rather than customized to obtain data ownership for specific scenarios. Summary of the Invention

[0010] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a data sharing system, method, storage medium and device based on blockchain privacy encryption, which can solve the above-mentioned problems.

[0011] Design Principles: This paper proposes a data sharing technology based on homomorphic encryption technology with security and privacy protection of blockchain sidechain. It utilizes homomorphic encryption and differential privacy technology in cryptography and includes consortium nodes, privacy computing, data nodes, multi-party secure computing scheduling, homomorphic encryption technology, secret sharing and OT protocol.

[0012] Explanation of terms used in the plan:

[0013] MPC (Secure Multi-Party Computation) is a multi-party computation algorithm that protects data security and privacy.

[0014] OT-Oblivious Transfer is a secure option and transmission protocol.

[0015] Overall solution: In order to solve the above problems, the overall design solution of this application is as follows.

[0016] A data sharing system based on blockchain privacy encryption, the data sharing system comprising:

[0017] The data node data storage module is used to store the data of each alliance member;

[0018] The data verification module includes an authorization unit and a verification unit. The verification unit verifies whether the sidechain can authorize data sharing and data modeling within the consortium, and the verification unit authenticates the identity and data assets of the data in the sidechain.

[0019] The hash data protection module performs privacy encryption, hash data modeling, and sharing on node data that has completed identity authentication and authorization verification through multi-party secure computation.

[0020] The user terminal module is used for local node data upload, shared data query application, and display.

[0021] Furthermore, each alliance member's corresponding blockchain sidechain includes data nodes, authorization nodes, verification nodes, and data model nodes. Currently, each node of the sidechain completes closed-loop control for data sharing and protection through multi-party secure computation.

[0022] Furthermore, each sidechain node of the consortium blockchain establishes a security mechanism based on its own data security level system, and establishes privacy and security through a federated model and multi-party computation.

[0023] Furthermore, the data model node control requires data storage, data acquisition, authentication, secure computation algorithms, and access control for each data sharing session to complete data submission and verification across the four nodes.

[0024] Furthermore, the data node stores data for the corresponding sidechain based on the data node data storage module.

[0025] Furthermore, the verification node and authorization node are based on a data verification module, used for identity authentication and data asset authentication in the sidechain, as well as authorization of whether data can be shared and data modeling.

[0026] Furthermore, the verification node binds identity and data assets with serial codes and determines the uniqueness of data assets through a one-to-many idempotent relationship, thereby preventing the generation of identical junk data assets.

[0027] This invention also discloses a data sharing method based on blockchain privacy encryption, the method comprising:

[0028] A sidechain node alliance is constructed, in which each data center and / or user, as an alliance member, puts its own data on the chain, establishes a security mechanism based on the security level system of its own data, and completes the construction of the sidechain alliance node through multi-party security computation.

[0029] Multi-party secure computation scheduling, each sidechain authenticates its own data nodes to ensure that the data sharing mode on the data nodes of the sidechain complies with the security mechanism;

[0030] After user authentication, the user joins the sidechain node alliance and sends an access request to the target sidechain. The target sidechain then pushes three access results to the user based on the security level of its own data: privacy encryption processing, hash data modeling, or sharing.

[0031] Compared to existing technologies, the advantages of this invention are as follows: This application combines the data of each node with a federated learning multi-party secure computation privacy architecture via a sidechain, verifies identity based on the sidechain, and builds a shared data model. Node data on the sidechain can achieve data sharing and maintain data consistency, while also ensuring the data security and privacy of important nodes based on federated learning multi-party secure computation privacy protection, preventing misuse. Furthermore, it allows for data modeling of key nodes based on the business application scenarios of other nodes, enabling other nodes to access relevant business scenario data even without ownership of the data. In practical application scenarios, the main value lies in the following:

[0032] 1) Alliance nodes can share personal privacy data through modeling;

[0033] 2) Data modeling can be used on alliance nodes to selectively share personal privacy data;

[0034] 3) For all alliance nodes, it can improve the data model and protect their own privacy data;

[0035] 4) It facilitates data utilization and data modeling, while protecting the privacy of both parties;

[0036] By adopting a sidechain-based data model for sharing, data utilization can be improved by 50%, and data privacy and security can be improved by 60%. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the data sharing system based on blockchain privacy encryption according to the present invention;

[0038] Figure 2 This is a system technical architecture diagram;

[0039] Figure 3 This is a diagram illustrating application scenarios.

[0040] Figure 4 This is a schematic diagram of the method. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] It should be understood that the terms "system," "module," "unit," and / or "assembly" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0043] First Embodiment

[0044] A data sharing system based on blockchain privacy encryption, see [link / reference] Figure 1 The data sharing system includes a data node data storage module, a data verification module, a hash data protection module, and a user terminal module.

[0045] The data node data storage module is used to store the data of each alliance member.

[0046] The data verification module includes an authorization unit and a verification unit. The verification unit verifies whether the sidechain can authorize data sharing and data modeling within the consortium, and the verification unit authenticates the identity and data assets of the data in the sidechain.

[0047] The hash data protection module performs privacy encryption, hash data modeling, and sharing on node data that has completed identity authentication and authorization verification through multi-party secure computation.

[0048] The user terminal module is used for uploading data to local nodes and requesting and displaying shared data queries. User terminals include, but are not limited to, mobile phones, computers, and tablets.

[0049] Among them, see Figure 2 In the system technology architecture diagram, the system utilizes homomorphic encryption and differential privacy technologies in cryptography. Specifically, the blockchain sidechains corresponding to each alliance member include: data nodes, authorization nodes, verification nodes, and data model nodes; multi-party secure computation and the technologies used between them (privacy technologies, homomorphic encryption, secret sharing, OT protocol technology, etc.). This technology mainly describes how secure privacy computation technology completes the closed loop of data sharing and protection on the blockchain sidechain.

[0050] Furthermore, each sidechain node of the consortium blockchain establishes a security mechanism based on its own data security level system, and establishes privacy and security through a federated model and multi-party computation.

[0051] Furthermore, the data model node controls each data sharing process, requiring data storage, data acquisition, authentication, secure computation algorithms, and access control to complete data submission and verification across the four nodes. The data model node primarily builds the shared data model, unifying the shared data across all nodes for data modeling. All nodes can use and construct this data model through authorized nodes. This node also needs to verify the data on the data model through verification nodes to ensure the uniqueness of data entering the data model node.

[0052] Furthermore, the data node stores data on the corresponding sidechain based on the data node data storage module. This data node is similar to those used in everyday applications such as financial institutions, public service agencies, and telecommunications regulatory agencies.

[0053] Furthermore, the verification node and authorization node are based on a data verification module, used for identity authentication and data asset authentication in the sidechain, as well as authorization of whether data can be shared and data modeling.

[0054] The verification node binds identity and data assets with serial codes, and uses a one-to-many idempotent relationship to determine the uniqueness of data assets, preventing the generation of identical data assets and thus avoiding the generation of duplicate junk data assets.

[0055] Authorized nodes are the entities within a sidechain that authorize data sharing and data modeling within the consortium. Authorized data node users can share data assets and perform data modeling on the sidechain. Without authorized nodes, data asset sharing and data modeling are impossible, especially in cases involving privacy data protection, where the emphasis on authorization is paramount.

[0056] Blockchain sidechain security and privacy computing technology is mainly used to synchronize and encrypt data on the sidechain nodes after a consortium of nodes is formed on the sidechain. It can also selectively share data on the blockchain sidechain, complete data modeling and sharing through homomorphic encryption, and prevent other nodes on the sidechain from tampering with and stealing the data through privacy encryption.

[0057] Application example: See Figure 3This technological field can be used to form a sidechain connecting different banking and financial institutions, with each bank serving as a node. This allows each institution to assess the security of its business by checking for existing loan data, non-performing loan records, and blacklists of its customers before granting loans. It can also be applied in government and public services, where sidechain nodes can aggregate user data models from different departments. These models can then be refined by various departmental nodes, enabling them to utilize the data for various business applications.

[0058] Sidechains are primarily used for establishing nodes across different data departments. In a data sharing or encryption process within a pre-loan collaborative modeling and scoring business scenario, financial institutions first need to be added to the same sidechain, forming data nodes on the sidechain. Then, a data storage mechanism is established on the sidechain nodes, and a data model is built according to privacy-preserving computation rules. The methods for data sharing and privacy encryption are completely different. For the sharing mechanism, a data model can be built based on each sidechain node, and a secret sharing mechanism can be established on the data model to ensure data sharing.

[0059] For the data encryption mechanism model, each sidechain node establishes a security mechanism based on its own data security level system. Privacy and security are established through a federated model and multi-party computation. While ensuring that the data is used, the sidechain node only knows the result of the entire model data, rather than the traceability and information of the data, thereby ensuring the privacy and security of the data.

[0060] As shown in the technical architecture diagram, the sidechain consists of data nodes, verification nodes, authorization nodes, and data model nodes. The multi-party secure computation module serves as a technical capability module (referred to as "multi-party secure computation scheduling" in the technical architecture diagram), facilitating its invocation in specific business scenarios to achieve customizable shared privacy data. This data sharing and encryption only involves sidechain business logic and does not require transactions on the main chain; that is, this technology does not involve the main chain. The sidechain's data model nodes control the submission and verification of four nodes (data storage, data acquisition, identity verification, secure computation algorithm, and access control) for each data sharing. In other words, data nodes on the sidechain must first complete identity authentication and access control. Only after each data node's confirmation can data sharing or encryption processing proceed. Within any node, if data needs to be shared or shared with other nodes' data, identity authentication and access control from those other nodes are required before the establishment of the data model for data acquisition can proceed. Furthermore, the hash value of the corresponding data model is recorded in the block module, forming a data hash value, and a block hash value is formed. This hash value is serialized into a hash value stream and shared with other data nodes through authorized nodes on the sidechain. If a data node needs to protect its own data from infringement, i.e., from modification or alteration of the data model by other data nodes on the sidechain, the data node needs to perform encrypted computation on its own data through external multi-party secure computation (MPC) protocol. This computation process is based on homomorphic encryption, secret sharing, and over-the-air (OT) implementation, thereby ensuring that its own data forms a consensus within the sidechain and is encrypted. Moreover, it can complete the data model node process while sharing data, allowing users to understand the application scenario and results of the data usage without being aware of the data creation process. For example, Bank A wants to know Xiaoming's information, and Xiaoming's information is collected through Bank B. At this point, Bank A can learn through the data model node that Xiaoming is 28 years old, male, has worked for 3 years, holds a doctoral degree, and enjoys sports. However, Bank A only knows this information through the data model node, but it has no idea how this information was obtained. It may be a data model built by other data nodes on the sidechain, or it may be a data model node built by another bank. In another scenario, the data in the data node is completely closed, not shared externally, and can only be used by the node itself. In this case, the data node needs to perform privacy data protection through external multi-party secure computation, encrypting the data with a password. Other data nodes cannot know the decryption key and therefore cannot decrypt the data, thus ensuring that the data can only be used by the node itself. After external parties obtain the data through the sidechain alliance, it is also an encrypted hash value and cannot be used by the value.

[0061] Second Embodiment

[0062] A data sharing method based on blockchain privacy encryption, see [link to relevant documentation]. Figure 4 The methods include:

[0063] A sidechain node alliance is constructed, in which each data center and / or user, as an alliance member, puts its own data on the chain, establishes a security mechanism based on the security level system of its own data, and completes the construction of the sidechain alliance node through multi-party security computation.

[0064] Multi-party secure computation scheduling, each sidechain authenticates its own data nodes to ensure that the data sharing mode on the data nodes of the sidechain complies with the security mechanism;

[0065] After user authentication, the user joins the sidechain node alliance and sends an access request to the target sidechain. The target sidechain then pushes three access results to the user based on the security level of its own data: privacy encryption processing, hash data modeling, or sharing.

[0066] In the process of secure sharing and privacy encryption protection of sidechain data nodes, each data node has three ways of accessing data: sharing, data modeling, and privacy protection.

[0067] During the sharing process, after storing the data, the data nodes will verify the data. After verifying the uniqueness of the data, the verified data will be protected by hash data. In the data sharing capability, the data needs to be uniformly regulated by data rules. When sharing data, each data node needs to clean the data according to different sidechain permissions and apply the data to its own node.

[0068] In data modeling, data nodes encapsulate the stored data and transmit it through the transport layer (responsible for communication between two processes on the host) to the multi-party secure computation framework (see application scenario diagram). The multi-party secure computation framework sends computation invitations to nodes in the sidechain, employing different algorithm strategies based on the number of sidechain nodes to notify nodes to perform data privacy encryption operations. Throughout the computation protocol, the computation logic is publicly accessible, preventing others from accessing or modifying the original data, thus protecting the rights and value of data nodes and the data itself. Participants only need to participate in the computation protocol to complete the data computation without relying on third parties, and the computation results cannot be used to deduce the original data, ensuring data security. Furthermore, while acquiring data, the multi-party secure computation framework initiates an MPC computation task scheduling for the sidechain nodes. After authorization and permission confirmation through the sidechain, the framework sends the original data, local network card IP address, and server IP address to each node through the network layer, allowing data model nodes to know which data nodes the data originated from for collaborative computation. It also searches the sidechain for other data nodes holding similar data types to perform secure system computations. The data nodes participating in the system computation will acquire data according to the computational logic and jointly perform system computation on the data using MPC computation tasks. While ensuring data privacy and user rights, all parties receive accurate data feedback, and node data is not leaked to any data node participant throughout the process. Depending on the number of nodes, it can be divided into 2PC (with only two participants) and general MPC (with multiple participants, ≥3). To ensure the scalability of the system, the minimum number of participants is two, and the MPC algorithm is used. This allows multiple data owners to collaborate on computations without knowing each other's data sources, outputting the final result and completing data modeling.

[0069] In privacy protection, when a data node does not want to share or model data on the sidechain, it performs privacy-preserving computations to encrypt its own data. To ensure the security and uniqueness of its data, it encrypts it, forming a hash block. During the data upload process, the data node first controls its own data to be off-chain through the permission node. After invoking privacy computation, it encrypts the data, forming a new data module. According to the sidechain's data rules, the encryption result is synchronously transmitted to all data nodes, forming a unified ledger. However, because the data is encrypted, even if other data nodes obtain the data, they cannot use it for application scenarios without the decryption key, thus protecting privacy.

[0070] In summary, for privacy protection in sidechain data sharing, the first step is to authenticate the data nodes on the sidechain to ensure their integrity. Then, during data sharing, data nodes can share data in three ways based on its importance: sharing, data modeling, and privacy. This approach ensures data security, preventing misuse and tampering, and also supports data modeling in public service and financial sectors, enabling a better data sharing loop.

[0071] Third Embodiment

[0072] A computer storage medium storing computer instructions is characterized in that the computer instructions execute the aforementioned method when run. The method is described in detail in the foregoing section and will not be repeated here.

[0073] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including permanent and non-permanent, removable and non-removable media. Information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0074] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.; conventional procedural programming languages ​​such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0075] Fourth embodiment

[0076] The present invention also provides an apparatus comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the aforementioned method when executing the computer instructions. The method is described in detail in the foregoing section and will not be repeated here.

[0077] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data sharing system based on blockchain privacy encryption, characterized in that, The data sharing system includes: The data node data storage module is used to store the data of each alliance member; The data verification module includes an authorization unit and a verification unit. The verification unit verifies whether the sidechain can authorize data sharing and data modeling within the consortium, and the verification unit authenticates the identity and data assets of the data in the sidechain. The hash data protection module performs privacy encryption, hash data modeling, and sharing on node data that has completed identity authentication and authorization verification through multi-party secure computation. Multi-party secure computation includes privacy technology, homomorphic encryption, secret sharing, and OT protocol technology. The multi-party secure computation allows multiple data owners to perform collaborative computation without knowing each other's data sources. The computation logic is publicly available, and the participants in the computation participate in the computation protocol. Data computation can be completed without relying on a third party, and the participating parties cannot infer the original data from the computation results. The user terminal module is used for local node data upload and shared data query application and display; Each alliance member's corresponding blockchain sidechain includes data nodes, authorization nodes, verification nodes, and data model nodes. Currently, each node on the sidechain completes closed-loop control for data sharing and protection through multi-party secure computation. This data sharing and encryption only involves the sidechain business logic and does not require transaction scenarios on the main chain, thus not involving the main chain. Each alliance blockchain's sidechain node establishes a security mechanism based on its own data security level system, and establishes privacy and security through a federated model and multi-party computation. The data model node is mainly used to build a shared data model. It unifies the data shared by various nodes and performs data modeling on this node. All nodes can use and build the data through the authorized node. This node also needs to verify the data on the data model through the verification node to ensure the uniqueness of the data entering the data model node. The data model node controls each data sharing process to perform data storage, data acquisition, identity verification, secure computation algorithms, and access control, in order to complete the data submission and verification of the four nodes: data node, authorization node, verification node, and data model node. The verification node binds identity and data assets with serial codes and determines the uniqueness of data assets through a one-to-many idempotent relationship, preventing the generation of identical junk data assets.

2. The data sharing system according to claim 1, characterized in that: The data node stores data on the corresponding sidechain based on the data node data storage module.

3. The data sharing system according to claim 1, characterized in that: The verification node and authorization node are based on the data verification module and are used for identity authentication and data asset authentication in the sidechain, as well as authorization of whether data can be shared and data modeling.

4. A data sharing method based on blockchain privacy encryption, characterized in that, The methods include: A sidechain node alliance is constructed, in which each data center and / or user, as an alliance member, puts their own data on the chain, establishes a security mechanism based on the security level of their own data, and completes the construction of the sidechain alliance nodes through multi-party secure computation. Multi-party secure computation includes privacy technology, homomorphic encryption, secret sharing, and OT protocol technology. The multi-party secure computation allows multiple data owners to perform collaborative computation without knowing each other's data sources. The computation logic is publicly disclosed, the participants in the computation participate in the computation protocol, and the data computation can be completed without relying on a third party. Furthermore, the participants cannot infer the original data from the computation results. Multi-party secure computation scheduling, each sidechain authenticates its own data nodes to ensure that the data sharing mode on the data nodes of the sidechain complies with the security mechanism; User access request: After user authentication, the user joins the sidechain node alliance and sends an access request to the target sidechain. The target sidechain pushes three access results to the user based on its own data security level: privacy encryption processing, hash data modeling, or sharing. Each alliance member's corresponding blockchain sidechain includes data nodes, authorization nodes, verification nodes, and data model nodes. Currently, each node on the sidechain completes closed-loop control for data sharing and protection through multi-party secure computation. This data sharing and encryption only involves the sidechain business logic and does not require transaction scenarios on the main chain, thus not involving the main chain. Each alliance blockchain's sidechain node establishes a security mechanism based on its own data security level system, and establishes privacy and security through a federated model and multi-party computation. The data model node is mainly used to build a shared data model. It unifies the data shared by various nodes and performs data modeling on this node. All nodes can use and build the data through the authorized node. This node also needs to verify the data on the data model through the verification node to ensure the uniqueness of the data entering the data model node. The data model node controls the data storage, data acquisition, authentication, secure computing algorithms, and access control required for each data sharing session, in order to complete the data submission and verification of the four nodes: data node, authorization node, verification node, and data model node. The verification node binds identity and data assets with serial codes and determines the uniqueness of data assets through a one-to-many idempotent relationship, thus preventing the generation of identical junk data assets.

5. A computer storage medium storing computer instructions thereon, characterized in that: The computer instructions execute the method of claim 4 when they are run.

6. An apparatus comprising a memory and a processor, the memory storing computer instructions executable on the processor, characterized in that: The processor executes the method of claim 4 when running the computer instructions.

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