Data delivery method, device, medium and system

Abstract

The invention provides a data delivery method, device, medium and system, and the method comprises the steps: determining a target privacy calculation protocol according to a data type and a privacy level before data delivery, or determining the target privacy calculation protocol according to the data type; binding a data use constraint, a privacy calculation parameter and a participant digital identity by adopting an intelligent contract; and according to the target privacy calculation protocol and the bound smart contract, constructing a data delivery application program, and executing the data delivery application program to complete data delivery, the data delivery application program being an application program matched with the data product type. The problem that in the prior art, a unified application program development standard is not formed, so that data delivery tools are fragmented, and compatibility is poor is solved.

Description

Data delivery methods, apparatus, media and systems Technical Field

[0001] This application relates to the field of privacy computing technology, and more specifically, to a data delivery method, a data delivery apparatus, a computer-readable storage medium, and a data delivery system. Background Technology

[0002] Trusted Data Space: It is a data circulation and utilization infrastructure based on consensus rules, connecting multiple entities to achieve data resource sharing and utilization. It is an application ecosystem for the co-creation of data element value and an important carrier to support the construction of a national integrated data market.

[0003] The lack of unified application development standards in current technologies leads to fragmented data delivery tools and poor compatibility. Direct delivery methods struggle to guarantee data privacy, especially in privacy-preserving computing scenarios where security requirements cannot be met. Customized development necessitates reinventing the wheel, preventing developers from quickly building applications that meet diverse delivery needs. The dispersed nature of delivery tools and the lack of a centralized management platform make it difficult for users to associate and install suitable applications. Summary of the Invention

[0004] The main objective of this application is to provide a data delivery method, a data delivery device, a computer-readable storage medium, and a data delivery system, so as to at least solve the problem that the lack of a unified application development standard in the prior art leads to the fragmentation of data delivery tools and thus poor compatibility.

[0005] To achieve the above objectives, according to one aspect of this application, a data delivery method is provided, comprising: determining a target privacy computing protocol based on data type and privacy level, or determining a target privacy computing protocol based on the data type, before data delivery; binding data usage constraints, privacy computing parameters, and participant digital identities using a smart contract; constructing a data delivery application based on the target privacy computing protocol and the bound smart contract, and executing the data delivery application to complete data delivery, wherein the data delivery application is an application that matches the data product type.

[0006] Optionally, the target privacy computing protocol is determined based on the data type and privacy level, including: when the data type is real-time streaming data and the privacy level is less than a preset level, the target privacy computing protocol is determined to be a multi-party computing protocol;

[0007] Determining the target privacy computing protocol based on the data type includes, in the case that the data type is a metadata product, determining the target privacy computing protocol, including semantic annotation and quality indicator verification methods.

[0008] Optionally, in the process of using smart contracts to bind data usage constraints, privacy calculation parameters, and the digital identities of participants, the method further includes: embedding data watermarking and document fingerprint verification mechanisms in the smart contract.

[0009] Optionally, during the execution of the data delivery application, the method further includes: for relational databases, using middleware technology to filter query statements; and for graph data, using attribute graph models and subgraph schema hiding techniques to protect the graph data.

[0010] Optionally, during the execution of the data delivery application, the method further includes: creating a data sandbox within a trusted execution environment, the data sandbox including memory access pattern obfuscation operations and critical variable address randomization operations; restricting the application's access to the target memory region so that data is processed within the sandbox; and verifying the integrity of the isolated execution region before each data processing.

[0011] Optionally, restrictions may be placed on the application's access to the target memory region, including: adjusting the application's memory access permissions so that only authorized code segments can access the data;

[0012] In the process of restricting application access to the target memory region, the method further includes: using memory encryption technology to encrypt the data within the data sandbox.

[0013] Optionally, the method further includes: recording all operations using audit logs, and displaying the audit logs in a visual manner after data delivery is completed.

[0014] According to another aspect of this application, a data delivery apparatus is provided, comprising: a first processing unit, configured to determine a target privacy computing protocol based on data type and privacy level, or, to determine a target privacy computing protocol based on the data type, before data delivery; a second processing unit, configured to bind data usage constraints, privacy computing parameters, and participant digital identities using a smart contract; and a third processing unit, configured to construct a data delivery application based on the target privacy computing protocol and the bound smart contract, and execute the data delivery application to complete data delivery, wherein the data delivery application is an application that matches the data product type.

[0015] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0016] According to another aspect of this application, a data delivery system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0017] By applying the technical solution of this application, the privacy routing engine integrated in the SDK can automatically select the most suitable privacy computation protocol based on the data type and privacy level. This mechanism avoids the complexity of developers manually selecting or adapting privacy computation protocols, while also ensuring the security and compliance of the data delivery process. For example, for highly sensitive data, the system will automatically route it to a TEE (Trusted Execution Environment) for processing, while for real-time streaming data, a lightweight MPC (Multi-Party Secure Computation) protocol will be selected. By binding data usage constraints, privacy computation parameters, and the digital identities of participating parties through smart contracts, the SDK ensures the immutability and enforceability of these core elements during the data delivery process. The use of smart contracts not only enhances the compliance and security of data delivery but also provides a reliable foundation for auditing and traceability through blockchain technology, further improving the transparency and credibility of the entire data delivery process. The integration of the SDK with the application center provides developers with a platform for centralized management, distribution, and updating of data delivery applications. This not only simplifies the application distribution process but also enables users to easily obtain and install applications that meet various data delivery needs, greatly improving the compatibility and efficiency of data delivery tools. This solves the problem of poor compatibility caused by the lack of a unified application development standard in existing technologies, which leads to fragmentation of data delivery tools. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 shows a flowchart illustrating a data delivery method according to an embodiment of this application;

[0020] Figure 2 shows a structural block diagram of a data delivery apparatus provided according to an embodiment of this application. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] As described in the background section, the lack of a unified application development standard in existing technologies leads to fragmented data delivery tools and poor compatibility. Direct delivery methods struggle to guarantee data privacy, especially in privacy-preserving computing scenarios where security requirements cannot be met. Customized development necessitates reinventing the wheel, preventing developers from quickly building applications that meet diverse delivery needs. The dispersed delivery tools and lack of a centralized management platform make it difficult for users to associate and install suitable applications. To address the problem of fragmented data delivery tools and poor compatibility caused by the lack of a unified application development standard in existing technologies, embodiments of this application provide a data delivery method, a data delivery apparatus, a computer-readable storage medium, and a data delivery system.

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0026] This embodiment provides a data delivery method. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] It should be noted that the information (including but not limited to) user device information and user personal information, and the data (including but not limited to data used for display and analysis) collected in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, and necessary confidentiality measures have been taken. This process does not violate public order and good morals, and corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage. If the user chooses to agree, the user can view the purpose of data use in real time through the authorization interface and has the right to withdraw authorization or delete data at any time. After authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0028] Figure 1 is a flowchart of a data delivery method according to an embodiment of this application. As shown in Figure 1, the method includes the following steps:

[0029] Step S101: Before data delivery, determine the target privacy computing protocol based on the data type and privacy level, or determine the target privacy computing protocol based on the aforementioned data type.

[0030] Step S102: Use smart contracts to bind data usage constraints, privacy calculation parameters, and the digital identities of the participants;

[0031] Step S103: Based on the aforementioned target privacy computing protocol and the bound smart contract, construct a data delivery application and execute the data delivery application to complete data delivery. The data delivery application is an application that matches the type of data product.

[0032] SDK (Software Development Kit): A software development kit typically contains a set of development tools, libraries, and documentation to assist developers in developing applications on a specific software platform. SDKs can provide APIs (Application Programming Interfaces), code examples, debugging tools, etc., to help developers implement functionality more efficiently and reduce development workload.

[0033] TEE (Trusted Execution Environment) is an environment that provides a higher level of security than a regular operating system, allowing sensitive applications to run on a device. TEE ensures secure boot, isolated execution, and encrypted storage, and is often used to protect users' private data, authentication keys, and other important information from unauthorized access and tampering.

[0034] MPC (Multi-Party Computation): This is a protocol in cryptography that allows multiple participants to collaborate on a computation process without any party revealing their input data to the others.

[0035] Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0036] In the steps described above, the privacy routing engine integrated into the SDK automatically selects the most suitable privacy computation protocol based on the data type and privacy level. This mechanism avoids the complexity of developers manually selecting or adapting privacy computation protocols, while also ensuring the security and compliance of the data delivery process. For example, for highly sensitive data, the system automatically routes it to a TEE (Trusted Execution Environment) for processing, while for real-time streaming data, it selects the lightweight MPC (Multi-Party Secure Computation) protocol. By binding data usage constraints, privacy computation parameters, and the digital identities of participating parties through smart contracts, the SDK ensures the immutability and enforceability of these core elements during the data delivery process. The use of smart contracts not only enhances the compliance and security of data delivery but also provides a reliable foundation for auditing and traceability through blockchain technology, further improving the transparency and credibility of the entire data delivery process. The integration of the SDK with the application center provides developers with a platform for centralized management, distribution, and updating of data delivery applications. This not only simplifies the application distribution process but also enables users to easily obtain and install applications that meet various data delivery needs, greatly improving the compatibility and efficiency of data delivery tools. This solves the problem of poor compatibility caused by the lack of a unified application development standard in existing technologies, which leads to fragmentation of data delivery tools.

[0037] The SDK takes into account the characteristics of different data types, including but not limited to API interface data, structured datasets, relational databases, file data, real-time streaming data, graph data, spatiotemporal data, multimedia data, time-series data, and metadata products. For each data type, the SDK provides dedicated modules and interfaces, ensuring that developers can easily develop corresponding applications for various data products, thereby achieving a unified development standard across data types.

[0038] Alternative solutions include: packaging the SDK as a Docker image, allowing developers to deploy and run applications via containerization; providing a low-code development platform where developers can configure data delivery logic through a visual interface without writing code; and supporting WebAssembly technology for cross-platform application development.

[0039] A Docker image is a collection of files containing all the dependencies and configurations needed to run a piece of software. It allows applications to run consistently in any environment, whether on a developer's laptop or a production server.

[0040] WebAssembly (often abbreviated as Wasm) is a binary instruction format designed to provide web applications with a near-native high-performance execution environment while maintaining the sandbox security features of the web.

[0041] By subscribing to data asset catalog change events through GraphQL, local routing policies can be dynamically updated; privacy computing resources (such as the number of TEE nodes) can be registered as a new type of resource in the middleware resource pool; GraphQL (GraphQL Query Language) is a query language that provides developers with a more efficient, powerful, and flexible way to acquire and manipulate data.

[0042] Federated learning optimization: Adaptive gradient pruning technique is adopted, combined with dynamic adjustment of noise injection amount (noise standard deviation σ is inversely proportional to privacy budget ε); secure multi-party summation is used to replace plaintext aggregation in the model aggregation stage.

[0043] In one embodiment of this application, determining the target privacy computing protocol based on data type and privacy level includes: when the data type is real-time streaming data and the privacy level is less than a preset level, determining the target privacy computing protocol as a multi-party computing protocol;

[0044] Determining the target privacy computing protocol based on the above data types includes: when the above data types are metadata products, determining the target privacy computing protocol includes methods for semantic annotation and quality indicator verification.

[0045] Different data types may have different security and privacy requirements. For example, real-time streaming data may prioritize computational speed and low latency, while metadata products may focus more on data accuracy, consistency, and semantic understanding. By matching the most suitable privacy-preserving computation protocol, an appropriate level of privacy protection can be provided without compromising data utility. The selection of a privacy-preserving computation protocol directly affects the type and amount of computing resources required. For example, federated learning may be more efficient for structured data; while for highly sensitive real-time streaming data, a lightweight MPC protocol can better balance security and performance. This approach ensures efficient resource utilization and avoids over-configuration or resource waste. The performance of the data delivery application directly impacts the end-user experience. For example, choosing an MPC protocol when processing real-time streaming data ensures timely and smooth data transmission, reducing waiting time and data processing latency. For metadata products, ensuring data consistency and accuracy allows users to trust the data source more, improving user satisfaction. Selecting a privacy-preserving computation protocol based on data type and privacy level better meets industry and regulatory requirements. For example, for metadata products involving personal privacy, semantic annotation and quality indicator verification not only ensure the correct use of data but also support subsequent audits and compliance checks. This intelligent selection mechanism encourages developers to explore and apply cutting-edge technologies best suited to specific data types and privacy needs. For instance, MPC acceleration solutions for real-time streaming data processing may drive technological advancements in related fields, while semantic understanding and smart contract binding of metadata products may lead to new trends in data governance and privacy protection.

[0046] In one embodiment of this application, the method further includes embedding a data watermark and document fingerprint verification mechanism in the smart contract during the process of using smart contracts to bind data usage constraints, privacy calculation parameters, and the digital identities of the participants.

[0047] Data watermarking technology embeds invisible markers into data, which can be used to trace the data's origin and usage rights. When data is transferred between different parties, watermarks ensure that the original attributes and ownership information of the data are preserved, thereby improving data traceability, which is particularly important for investigating and assigning responsibility for data breaches. Document fingerprints can be used to verify the integrity of data during transmission, ensuring that data has not been tampered with upon arrival at its destination. This is crucial for ensuring the trustworthiness of the data delivery process, especially in blockchain-based environments, where document fingerprints can be stored on the blockchain for subsequent verification and auditing. Combined with smart contracts, data watermarks can be designed to include access control information, such as data usage permissions and usage time windows. This allows smart contracts to perform fine-grained permission checks during data delivery and use, ensuring that data is used according to predefined rules and preventing unauthorized access or misuse. The use of data watermarks and document fingerprints can be combined with data anonymization and encryption technologies to further enhance privacy protection measures. For example, even if data is anonymized, watermarks can help identify the initial state of the data, preventing the recovery of sensitive information through reverse engineering.

[0048] In one embodiment of this application, during the execution of the above data delivery application, the method further includes: for relational databases, using middleware technology to filter query statements; and for graph data, using attribute graph models and subgraph pattern hiding techniques to protect the graph data.

[0049] For relational databases, an SQL proxy is used: The SQL proxy dynamically filters incoming SQL queries, preventing malicious attacks such as SQL injection and protecting the database from unauthorized access and data leakage risks. Role-based access control and field-level filtering ensure that query results only contain data that the user has the right to access, achieving data anonymization and further protecting data privacy. The SQL proxy can monitor database changes and capture these changes, supporting real-time data synchronization and historical data auditing, maintaining data consistency and integrity. Through Vitess sharding routing, the proxy can intelligently distribute query requests to relevant database shards, avoiding full table scans, reducing query response time, and improving the efficiency of data querying and delivery.

[0050] Vitess is a tool for horizontally scaling MySQL. It primarily uses sharding technology to distribute data storage and route queries, thereby improving database performance and scalability.

[0051] For graph data, the combination of attribute graph models and subgraph pattern hiding effectively protects the structure and attribute information of graph data, preventing the direct exposure of sensitive information. This technique, by increasing the complexity of the graph and performing k-anonymization, makes it difficult for attackers to infer specific individuals or relationships from the graph. Subgraph pattern hiding, by hiding specific subgraph structures or patterns, prevents deep analysis of the graph data, protects the privacy of nodes and edges in the graph, and meets the requirements of data protection regulations.

[0052] In one embodiment of this application, during the execution of the above-described data delivery application, the method further includes: creating a data sandbox within a trusted execution environment, the data sandbox including memory access pattern obfuscation operations and key variable address randomization operations; restricting the application's access to the target memory region so that data is processed within the sandbox; and verifying the integrity of the isolated execution region before each data processing.

[0053] Data sandboxing ensures data processing occurs within a TEE (Transparent Environment), which provides hardware-level isolation to prevent malware or operating system-level attacks. Memory access pattern obfuscation and critical variable address randomization further increase the unpredictability of data in memory, making it difficult to directly locate sensitive data even with physical access, thus effectively resisting side-channel attacks. By restricting application access to target memory regions, it ensures that only certified and authorized applications can access and process sensitive data, helping to protect the privacy of data subjects during data delivery and processing. Verifying the integrity of the Enclave before each data processing (ensuring that the code and data within the Enclave have not been tampered with or subjected to unauthorized access) guarantees that the application code has not been maliciously modified or implanted with backdoors, ensuring the security of the execution environment and the trustworthiness of the program. Enforcing compliance policies through technical means reduces the risk of data leakage and misuse. Since all operations are executed and logged within the TEE, this provides a reliable basis for auditing, allowing auditors to verify whether the data processing process complies with established privacy protection policies and compliance requirements.

[0054] In one embodiment of this application, restricting an application's access to a target memory region includes: adjusting the application's memory access permissions so that only authorized code segments can access the data;

[0055] In the process of restricting application access to the target memory region, the above method also includes: using memory encryption technology to encrypt the data in the data sandbox.

[0056] By restricting application access to specific memory regions, it is possible to effectively prevent malicious code or unauthorized components from accessing sensitive data, reducing the risk of data leakage. More granular memory access control compared to existing solutions can prevent memory overflows caused by flawed logic or vulnerabilities in applications, thus preventing attackers from exploiting such vulnerabilities to execute arbitrary code. Authorizing only necessary code snippets to access data can significantly reduce the potential attack surface, making it more difficult to find entry points for attacks.

[0057] In one embodiment of this application, the method further includes: recording all operations using audit logs, and displaying the audit logs in a visual manner after data delivery is completed.

[0058] Audit logs provide a detailed record of every operation during the data delivery process, including data reading, transmission, computation, result generation, and final delivery. This makes the entire data flow path clearly visible, facilitating subsequent auditing and ensuring that each step complies with relevant privacy and security requirements.

[0059] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0060] This application also provides a data delivery apparatus. It should be noted that the data delivery apparatus of this application can be used to execute the data delivery method provided in this application. This apparatus is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0061] The following describes the data delivery apparatus provided in the embodiments of this application.

[0062] Figure 2 is a structural block diagram of a data delivery device according to an embodiment of this application. As shown in Figure 2, the device includes: a first processing unit 21, configured to determine a target privacy computing protocol based on the data type and privacy level before data delivery, or to determine the target privacy computing protocol based on the aforementioned data type; a second processing unit 22, configured to bind data usage constraints, privacy computing parameters, and participant digital identities using a smart contract; and a third processing unit 23, configured to construct a data delivery application based on the aforementioned target privacy computing protocol and the bound smart contract, and to execute the aforementioned data delivery application to complete data delivery, wherein the aforementioned data delivery application is an application that matches the data product type.

[0063] The privacy routing engine integrated into the SDK automatically selects the most suitable privacy computation protocol based on the data type and privacy level. This mechanism avoids the complexity of developers manually selecting or adapting privacy computation protocols, while also ensuring the security and compliance of the data delivery process. For example, for highly sensitive data, the system automatically routes it to a TEE (Trusted Execution Environment) for processing, while for real-time streaming data, it selects the lightweight MPC (Multi-Party Secure Computation) protocol. By binding data usage constraints, privacy computation parameters, and the digital identities of participating parties through smart contracts, the SDK ensures the immutability and enforceability of these core elements during data delivery. The use of smart contracts not only enhances the compliance and security of data delivery but also provides a reliable foundation for auditing and traceability through blockchain technology, further improving the transparency and credibility of the entire data delivery process. The integration of the SDK with the application center provides developers with a platform for centralized management, distribution, and updating of data delivery applications. This not only simplifies the application distribution process but also enables users to easily obtain and install applications that meet various data delivery needs, greatly improving the compatibility and efficiency of data delivery tools. This solves the problem of poor compatibility caused by the lack of a unified application development standard in existing technologies, which leads to fragmentation of data delivery tools.

[0064] In one embodiment of this application, the first processing unit includes: a first processing module configured to determine that the target privacy computing protocol is a multi-party computing protocol when the data type is real-time streaming data and the privacy level is less than a preset level;

[0065] The first processing unit includes: a second processing module used to determine the target privacy computing protocol, including semantic annotation and quality indicator verification methods, when the above data type is a metadata product.

[0066] In one embodiment of this application, the second processing unit includes: a third processing module for embedding a data watermark and document fingerprint verification mechanism in the smart contract during the process of using smart contract-bound data usage constraints, privacy calculation parameters, and participant digital identities.

[0067] In one embodiment of this application, the third processing unit includes: a fourth processing module used to perform query statement filtering processing for relational databases using middleware technology during the execution of the above-mentioned data delivery application; and a fifth processing module used attribute graph model and subgraph pattern hiding technology to protect the graph data.

[0068] In one embodiment of this application, the third processing unit includes: a sixth processing module for creating a data sandbox within a trusted execution environment during the execution of the aforementioned data delivery application, wherein the data sandbox includes memory access pattern obfuscation operations and key variable address randomization operations; a seventh processing module for restricting the application's access to the target memory region so that data is processed within the sandbox; and an eighth processing module for verifying the integrity of the isolated execution region before each data processing.

[0069] In one embodiment of this application, the seventh processing module includes: a first processing submodule configured to adjust the memory access permissions of the aforementioned application so that only authorized code segments can access the data;

[0070] The seventh processing module includes: a second processing submodule used to encrypt the data in the aforementioned data sandbox by employing memory encryption technology during the process of restricting application access to the target memory region.

[0071] In one embodiment of this application, the above-mentioned apparatus further includes: a fourth processing unit for recording all operations using audit logs, and displaying the audit logs in a visual manner after the data delivery is completed.

[0072] The aforementioned data delivery device includes a processor and a memory. The first processing unit, the second processing unit, and the third processing unit are all stored as program units in the memory, and the processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0073] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, the lack of a unified application development standard in current technologies can be addressed, leading to fragmented data delivery tools and poor compatibility.

[0074] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0075] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the data delivery method.

[0076] This invention provides a processor for running a program, wherein the program executes the data delivery method described above.

[0077] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: before data delivery, determining a target privacy computing protocol based on the data type and privacy level, or determining the target privacy computing protocol based on the aforementioned data type; binding data usage constraints, privacy computing parameters, and the digital identities of participating parties using a smart contract; constructing a data delivery application based on the aforementioned target privacy computing protocol and the bound smart contract, and executing the data delivery application to complete data delivery. The data delivery application is an application that matches the type of data product. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0078] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: determining a target privacy computing protocol based on the data type and privacy level, or determining the target privacy computing protocol based on the aforementioned data type, before data delivery; binding data usage constraints, privacy computing parameters, and participant digital identities using a smart contract; constructing a data delivery application based on the aforementioned target privacy computing protocol and the bound smart contract, and executing the aforementioned data delivery application to complete data delivery, wherein the aforementioned data delivery application is an application that matches the data product type.

[0079] This application also provides a data delivery system, including: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any of the above-described methods.

[0080] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0081] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0085] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0086] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0087] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0088] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0090] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A data delivery method, characterized in that, include: Before data delivery, a target privacy computing protocol is determined based on the data type and privacy level, or the target privacy computing protocol is determined based on the data type. The system employs smart contracts to bind data usage constraints, privacy computation parameters, and the digital identities of participating parties. Based on the target privacy computation protocol and the bound smart contract, a data delivery application is constructed and executed to complete data delivery. The data delivery application is an application that matches the type of data product.

2. The method according to claim 1, characterized in that, Determining the target privacy computing protocol based on data type and privacy level includes: when the data type is real-time streaming data and the privacy level is less than a preset level, determining the target privacy computing protocol as a multi-party computing protocol; determining the target privacy computing protocol based on the data type includes: when the data type is metadata product, determining the target privacy computing protocol to include semantic annotation and quality indicator verification methods.

3. The method according to claim 1, characterized in that, In the process of using smart contracts to bind data usage constraints, privacy calculation parameters, and the digital identities of participants, the method further includes: embedding data watermarking and document fingerprint verification mechanisms in the smart contract.

4. The method according to claim 1, characterized in that, In the process of executing the data delivery application, the method further includes: for relational databases, using middleware technology to filter query statements; for graph data, using attribute graph model and subgraph schema hiding technology to protect the graph data.

5. The method according to claim 1, characterized in that, In the process of executing the data delivery application, the method further includes: creating a data sandbox within a trusted execution environment, the data sandbox including memory access pattern obfuscation operations and critical variable address randomization operations; restricting the application's access to the target memory region so that data is processed within the sandbox; and verifying the integrity of the isolated execution region before each data processing.

6. The method according to claim 5, characterized in that, Restricting application access to the target memory region includes: adjusting the application's memory access permissions so that only authorized code segments can access the data; in the process of restricting application access to the target memory region, the method further includes: using memory encryption technology to encrypt the data in the data sandbox.

7. The method according to claim 1, characterized in that, The method further includes: recording all operations using audit logs, and displaying the audit logs in a visual manner after data delivery is completed.

8. A data delivery device, characterized in that, include: The first processing unit is used to determine the target privacy computing protocol based on the data type and privacy level before data delivery, or to determine the target privacy computing protocol based on the data type; the second processing unit is used to bind data usage constraints, privacy computing parameters and digital identities of participants using smart contracts. The third processing unit is used to construct a data delivery application based on the target privacy computing protocol and the bound smart contract, and to execute the data delivery application to complete data delivery. The data delivery application is an application that matches the type of data product.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A data delivery system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

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