Data processing method, device, system, equipment, storage medium and program product

Abstract

The present application discloses a data processing method, device, system, equipment, storage medium and program product, the method includes: constructing a privacy computing task based on the metadata of a data set provided by a data partner; sending the subtask to the computing node bound to the participant to which the target data set belongs according to the target data set involved in the subtask disassembled from the privacy computing task; wherein the participant is a data user or a data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute the task; sending the execution result of the privacy computing task to the data user according to the execution result of the subtask returned by the computing node. By combining the cloud platform capabilities and locally deployed computing nodes to realize the processing of privacy computing tasks, sensitive information is avoided from being uploaded to the cloud, the risk of data leakage is reduced, and the availability and security of privacy data processing are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, apparatus, system, device, storage medium and program product. Background Art

[0002] In the current data processing field, data circulation can achieve high-quality and efficient data computing, helping customers achieve computing goals in conjunction with external data. In order to protect the security and stability of customers' own data, how to balance data privacy protection and convenient data circulation is a key challenge.

[0003] At present, in order to utilize the powerful computing power and flexible resources provided by cloud computing, customers upload data to the cloud for computing and processing. Although certain protection strategies are adopted for the uploaded data, the direct control over sensitive information is weakened and the risk of sensitive information leakage is increased. In particular, there are data compliance issues in cross-border transmission. Summary of the invention

[0004] The purpose of the present application is to propose a data processing method, device, system, equipment, storage medium and program product to address the deficiencies of the above-mentioned prior art, and this purpose is achieved through the following technical solutions.

[0005] The first aspect of the present application provides a data processing method, the method comprising:

[0006] Constructing a privacy computing task based on the metadata of the data set provided by the data partners; the data partners include data users and data providers;

[0007] According to the target data set involved in the subtasks decomposed from the privacy computing task, the subtask is sent to the computing node bound to the participant to which the target data set belongs; wherein the participant is the data user or the data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute tasks;

[0008] According to the execution result of the subtask returned by the computing node, the execution result of the privacy computing task is sent to the data user.

[0009] Based on the data processing method described in the first aspect above, the present application has at least the following beneficial effects or advantages:

[0010] By deploying computing nodes in the local environment of the data and binding the data partners to the computing nodes, the metadata of the data set provided by the data partners can be used to build privacy computing tasks in the cloud. Since the computing nodes are nodes deployed locally by the data partners for executing tasks, and the nodes can access the original data of the data set in the local environment to execute tasks, the subtasks decomposed from the privacy computing tasks are sent to the computing nodes bound to the corresponding participants for execution, and then the execution results of the privacy computing tasks are fed back to the data users based on the execution results of the subtasks returned by the computing nodes, thereby completing the required data calculations and processing, so that the original data is always in the local environment and does not need to be transmitted to the cloud, thereby ensuring the privacy and compliance of the data.

[0011] A second aspect of the present application provides a data processing method, the method comprising:

[0012] Displaying a task configuration page according to the task plan selected by the user and the metadata of the data set provided by the data partner; the data partner includes the data user and the data provider, and the user is the user of the data user;

[0013] Receiving task configuration information input by the user on the task configuration page, the task configuration information including data fields involved in calculation and metadata corresponding to the data fields;

[0014] Send the task configuration information to the cloud platform, so that the cloud platform generates a privacy computing task according to the task configuration information and sends a subtask to the computing node bound to the participant to which the target data set belongs; wherein the subtask is a task decomposed from the privacy computing task, the target data set is the data set involved in the subtask, the computing node is a node locally deployed by the participant for executing the task, the computing node can access the original data of the target data set to execute the task, and the participant is the data user or the data provider;

[0015] Receive the execution result of the privacy computing task returned by the cloud platform, where the execution result of the privacy computing task is obtained by the cloud platform based on the execution result of the subtask returned by the computing node.

[0016] Based on the data processing method described in the second aspect above, the present application has at least the following beneficial effects or advantages:

[0017] By deploying computing nodes in the local environment of the data and binding the data partners to the computing nodes, the data users among the data partners can configure the tasks according to the task plan selected by the user and the metadata of the data set provided by the data partners, and complete the construction of the privacy computing tasks on the cloud platform. Since the computing nodes are nodes deployed locally by the data partners for executing tasks, the cloud platform sends the subtasks disassembled from the privacy computing tasks to the computing nodes bound to the corresponding participants to access the original data of the data set in the local environment for execution, and then the data users receive the execution results of the privacy computing tasks fed back by the cloud platform, so that the original data is always in the local environment and does not need to be transmitted to the cloud, thereby ensuring the privacy and compliance of the data. It can be seen that this application realizes the processing of privacy computing tasks by combining the capabilities of the cloud platform and the locally deployed computing nodes, avoiding uploading sensitive information to the cloud, reducing the risk of data leakage, and thus improving the availability and security of privacy data processing.

[0018] The third aspect of the present application provides a data processing system, the system comprising a cloud platform and a data partner, the data partner comprising a data user and a data provider, the data provider and the data user are both bound to a computing node, the computing node is a node locally deployed by the data partner for executing tasks;

[0019] The data user is used to display a task configuration page according to the task solution selected by the user and the metadata of the data set provided by the data partner; receive the task configuration information input by the user on the task configuration page; send the task configuration information to the cloud platform, wherein the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields;

[0020] The cloud platform is used to generate a privacy computing task according to the task configuration information, and send the subtask to the computing node bound to the participant to which the target data set belongs according to the target data set involved in the subtask decomposed from the privacy computing task, and send the execution result of the privacy computing task to the data user according to the execution result of the subtask returned by the computing node bound to the participant; the participant is the data user or the data provider, and the computing node bound to the participant can access the original data of the target data set to execute the task.

[0021] A fourth aspect of the present application provides a data processing device, the device comprising:

[0022] A task construction module, used to construct a privacy computing task based on the metadata of the data set provided by the data partner; the data partner includes the data user and the data provider;

[0023] A task execution module, used to send the subtasks involved in the target data set decomposed from the privacy computing task to the computing node bound to the participant to which the target data set belongs; wherein the participant is the data user or the data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute tasks;

[0024] A result feedback module is used to send the execution result of the privacy computing task to the data user among the data partners according to the execution result of the subtask returned by the computing node.

[0025] A fifth aspect of the present application provides a data processing device, the device comprising:

[0026] A task management module is used to display a task configuration page based on the task plan selected by the user and the metadata of the data set provided by the data partner, wherein the data partner includes a data user and a data provider, and the user is a user of the data user; receive the task configuration information entered by the user on the task configuration page, wherein the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields; send the task configuration information to the cloud platform, so that the cloud platform generates a privacy computing task according to the task configuration information and sends a subtask to the computing node bound to the participant to which the target data set belongs; wherein the subtask is a task decomposed from the privacy computing task, the target data set is the data set involved in the subtask, the computing node is a node locally deployed by the participant for executing tasks, the computing node can access the original data of the target data set to execute tasks, and the participant is the data user or the data provider;

[0027] The result acquisition module is used to receive the execution result of the privacy computing task returned by the cloud platform, and the execution result of the privacy computing task is obtained by the cloud platform according to the execution result of the subtask returned by the computing node.

[0028] The sixth aspect of the present application proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect above.

[0029] The seventh aspect of the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method described in the first aspect above.

[0030] In an eighth aspect, the present application proposes a computer program product, including a computer program, wherein the computer program is executed by a processor to implement the method described in the first aspect.

[0031] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 is a schematic diagram of the architecture of a data processing system according to an exemplary embodiment;

[0034] Figure 2 is a flow chart of an embodiment of a data processing method according to an exemplary embodiment;

[0035] Figure 3 is a schematic diagram of a task configuration page according to an exemplary embodiment;

[0036] Figure 4 is a schematic diagram of a data product page according to an exemplary embodiment;

[0037] Figure 5 is a flow chart of another data processing method according to an exemplary embodiment;

[0038] Figure 6 A schematic diagram of a data processing application scenario according to an exemplary embodiment;

[0039] Figure 7 is a structural schematic diagram of a data processing device according to an exemplary embodiment;

[0040] Figure 8 is a schematic structural diagram of another data processing device according to an exemplary embodiment;

[0041] Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an exemplary embodiment;

[0042] Fig.10 The figure is a schematic diagram of the structure of a storage medium according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of systems and methods consistent with some aspects of the present application as detailed in the appended claims.

[0044] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0045] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0046] In addition, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0047] As mentioned earlier, the existing data processing model is to upload data to the public cloud to utilize the powerful computing power and flexible resources provided by cloud computing. Although this model provides powerful computing power and flexibility, it weakens the customer's direct control over sensitive information, which brings the risk of leakage of sensitive information, especially in cross-border transmission, and also faces data compliance issues.

[0048] In order to solve the above technical problems, the present application proposes a data processing method, device, system, equipment, storage medium and program product, which process data by combining the capabilities of the cloud platform with locally deployed computing nodes, while protecting the privacy of sensitive information, it can also use the convenience brought by the cloud platform.

[0049] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The several specific embodiments listed can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0050] Embodiment 1

[0051] See also Figure 1 , Figure 1 An architecture diagram of a data processing system provided by an embodiment of the present specification is shown, the data processing system includes a cloud platform 100 and a data partner 200, the data partner 200 includes a data user 201 and a data provider 202, the data provider 202 and the data user 201 are both bound to a computing node, the computing node is a node locally deployed by the data partner 200 for executing tasks, in the embodiment of the present application, the computing node bound to the data provider 202 can access the original data of the data set provided by the data provider 202 to execute tasks, and the computing node bound to the data user 201 can access the original data of the data set provided by the data user 201 to execute tasks.

[0052] The data user 201 is used to display the task configuration page according to the task solution selected by the user and the metadata of the data set provided by the data partner 200, receive the task configuration information input by the user on the task configuration page, and send the task configuration information to the cloud platform 100, where the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields;

[0053] The cloud platform 100 is used to generate a privacy computing task based on the task configuration information, and send the subtask to the computing node bound to the participant to which the target data set belongs according to the target data set involved in the subtask decomposed from the privacy computing task, and send the execution result of the privacy computing task to the data user 201 based on the execution result of the subtask returned by the computing node. The participant is the data user 201 or the data provider 202.

[0054] The above-mentioned data partner 200 can be understood as a console client provided by the cloud platform 100 for users to use. The data partner 200 includes a data user 201 and at least one data provider 202 for data cooperative research and development. The data user 201 can be understood as a console client used as a data user, and the data provider 202 can be understood as a console client used as a data provider. In other words, the console client can be used as a data user or as a data provider, and the functions it provides are the same. The console client can be a browser, an APP (Application), or a web application such as an H5 (Hyper Text Markup Language 5, Hypertext Markup Language Version 5) application, or a light application (also known as a mini-program, a lightweight application) or a cloud application. The console client can be deployed in an electronic device and needs to rely on the device to run or some APPs in the device to run.

[0055] The cloud platform 100 can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. The server can be a cloud server for basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDNs), and big data and artificial intelligence platforms, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The backend of the console client is deployed on the cloud platform 100, that is, the console client serves as the console front end on the cloud platform. After the user opens a cloud account on the console client, the cloud platform 100 treats the user as a tenant of a console and allocates a console to the tenant. The functions of the consoles of each tenant are the same, but the operating environments are independent of each other.

[0056] In the above Figure 1 In the cloud platform 100, the data user 201 has a corresponding console, and the data provider 202 has a corresponding console.

[0057] By applying the scheme of the embodiments of this specification, the console is deployed on the cloud platform by combining the convenience of cloud computing and the data security of local computing, and the computing nodes for data processing are deployed locally, thereby realizing the data privacy computing task and improving the user experience under the premise of ensuring that the data does not leave the domain. In addition, by delegating part of the processing power to the local computing nodes, the dependence on the cloud platform can be reduced, thereby improving the elasticity of the system. When there is a traffic peak or network fluctuation, the local computing nodes can process data to ensure the continuity and stability of the service.

[0058] Embodiment 2

[0059] Figure 2 is a flow chart of an embodiment of a data processing method according to an exemplary embodiment. Figure 1 The cloud platform in the data processing system shown is the execution entity, such as Figure 2 As shown, the following steps 201 to 203 are included:

[0060] Step 201: construct a privacy computing task based on the metadata of the data set provided by the data partners, where the data partners include data users and data providers;

[0061] Step 202: According to the target data set involved in the subtask decomposed from the privacy computing task, the subtask is sent to the computing node bound to the participant to which the target data set belongs; wherein the participant is a data user or a data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute the task;

[0062] Step 203: Send the execution result of the privacy computing task to the data user based on the execution result of the subtask returned by the computing node.

[0063] Data partners can be understood as all participants in data cooperative research and development, which include data users and at least one data provider. Data users can use the data sets provided by data providers to implement privacy computing tasks, or data users can combine the data sets provided by data providers and their own data sets to implement privacy computing tasks.

[0064] A dataset can be understood as raw data stored in the local environment of a data partner, which may involve sensitive data that requires special protection, including but not limited to personal identity information, financial information, health records, personal communications, company secrets, or other private data. In other words, the dataset of a data user is stored in the local environment of the data user, and the dataset of a data provider is stored in the local environment of the data provider.

[0065] Metadata can be understood as the key information of a data set, which includes key information such as the data structure, data size, data creation date, data type, etc. of the data set, but does not include the data content of the data set itself. For example, metadata includes the data fields and data types of the data fields in the table in the data set, the creation date of the table, and the data size. The metadata can be shared among all data circulators on the cloud platform. Based on the metadata, the data circulators can understand what data is in the data set, but do not know the data content itself.

[0066] A privacy computing task refers to a task that performs various calculations or analyses on data while protecting data privacy. This task usually involves multiple data holders, and it is necessary to ensure that the data content of each data holder is not exposed during the calculation process.

[0067] The computing node can be understood as a local running node deployed in the local environment of the data partner. The node can access the data content of the data set in the local environment to process computing tasks related to privacy computing tasks, and can also be used to store metadata of the data set of the data partner. After the computing node is bound to the data partner, the cloud platform can transmit computing tasks related to privacy computing tasks to the computing node through the data partner for execution.

[0068] A subtask is a task decomposed from a privacy computing task. It is a step in the process of completing the privacy computing task. The subtask involves the target data set to be used.

[0069] It should be noted here that the privacy computing task can be broken down into multiple subtasks, and the cloud platform will send each subtask to the computing node bound to the corresponding participant for execution.

[0070] The execution result of the privacy computing task can be understood as the final result obtained based on the execution results of the subtasks.

[0071] For example, assume that the data partners include data provider A, data provider B and data user C. Data provider A provides dataset 1, data provider B provides dataset 2 and data user C provides dataset 3. The privacy computing task constructed based on the metadata of the datasets provided by these three parties is an intersection task. According to the intersection task, subtask 1 that needs to use dataset 1, subtask 2 that needs to use dataset 2 and subtask 3 that needs to use dataset 3 are decomposed. Therefore, subtask 1 is sent to the computing node bound to data provider A for execution, subtask 2 is sent to the computing node bound to data provider B for execution, and subtask 3 is sent to the computing node bound to data user C for execution. Then, the execution result of the intersection task is obtained based on the execution result returned by the computing node of data provider A, the execution result returned by the computing node of data provider B and the execution result returned by the computing node of data user C, and the execution result of the intersection task is fed back to data user C.

[0072] At this point, the above Figure 2The data processing flow shown in the figure deploys computing nodes in the local environment of the data and binds the data partners to the computing nodes, so that the metadata of the data set provided by the data partners can be used on the cloud to build privacy computing tasks. Since the computing nodes are nodes deployed locally by the data partners to execute tasks, and the nodes can access the original data of the data set in the local environment to execute tasks, the subtasks decomposed from the privacy computing tasks are sent to the computing nodes bound to the corresponding participants for execution, and then the execution results of the privacy computing tasks are fed back to the data users based on the execution results of the subtasks returned by the computing nodes, thereby completing the required data calculations and processing, so that the original data is always in the local environment and does not need to be transmitted to the cloud, thereby ensuring the privacy and compliance of the data.

[0073] The above-mentioned data processing method provided in the embodiment of the present application can be applied to, but is not limited to, application scenarios involving data processing in the fields of e-commerce services, educational services, legal services, medical services, conference services, social network services, financial product services, logistics services and navigation services, for example: scenarios of store search data processing in e-commerce services, scenarios of student learning data processing in educational services, scenarios of case impact data processing in legal services, etc., which are not limited here.

[0074] In an optional embodiment of the present application, the above step 201 constructs a privacy computing task based on the metadata of the data set provided by the data partner, including the following specific steps:

[0075] Receiving a purchase request from a data user for a data set from a data provider;

[0076] In response to the purchase request, a privacy computing task is constructed based on the metadata of the data set of the data partner.

[0077] A purchase request can be understood as a data purchase request made by a data user among data partners before using the data of other data holders, in order to obtain data usage rights from other data holders.

[0078] That is to say, when the cloud platform receives a purchase request from a data user for a data set of a data holder, it can treat the data holder as a data provider for data cooperation and create a purchasing relationship between the data user and the data provider. Based on this purchasing relationship, the cloud platform allows the data user to use the metadata of the data provider's data set for data cooperation research and development.

[0079] For example, suppose user A needs to use user B's dataset. The cloud platform receives a purchase request from user A as a data user for user B's dataset. In response to the purchase request, the cloud platform builds a privacy computing task for the data user based on the metadata of user B's dataset.

[0080] In this embodiment, a data purchasing environment is provided on the cloud platform to process the purchase requests of data users for data providers' data sets to improve the efficiency of data purchasing, and privacy computing tasks are constructed according to the purchase requests to ensure the effectiveness and availability of the tasks.

[0081] In an optional embodiment of the present application, the process of constructing a privacy computing task based on the metadata of the data set of the data partner includes the following specific steps:

[0082] According to the task plan selected by the data user, the page information of the task configuration page is sent to the data user, and the page information includes the metadata of the data set of the data partner;

[0083] A privacy computing task is generated based on the task configuration information returned by the data user. The task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields.

[0084] Task plans can be understood as the types of tasks that can be constructed by the cloud platform for users. Different task plans implement different data processing requirements. Data users can choose task plans according to their own needs to construct privacy computing tasks.

[0085] The task configuration page can be understood as a page provided by the cloud platform for the task plan and displayed to the data user. This page contains configuration items that require user input, relevant information about the data set of the data provider in the purchase request, etc.

[0086] For example, see Figure 3 The task configuration page shown in the figure contains the input configuration items and output configuration items of the privacy computing task. In the input configuration items, users need to configure the data source, data set name, collision field, and users can add configuration items according to their own needs. In the data source option box, users are provided with options for data providers and data users. Figure 3 It can be seen that the user selects data provider A in the Data Source option box, and then the Dataset Name option box provides the user with options for the data sets included in the selected data source. Figure 3 It can be seen that the user selects Dataset 1 in the Dataset Name option box, and then the Collision Field option box provides the user with options for data fields included in the metadata of the dataset. Figure 3 It can be seen that the user selected shop_id in the collision field option box; in the output configuration item, the user needs to configure the data recipient, the output data table name, and the input fields corresponding to the field names in the output data table. The data recipient option box provides the user with options for data providers and data users. Figure 3 It can be seen that the user selects the data user in the data recipient option box, enters "Card 1" in the output data table name input box as the result data table output, and the user fills in "Dataset 1 / shop_id" for the "shop_id" field in the output data table.

[0087] Task configuration information can be understood as the data information configured by the user on the task configuration page received by the data user, which includes the data fields involved in the calculation and the metadata corresponding to the data fields. Figure 3 As shown, the data fields involved in the calculation are shop_id, and the corresponding metadata is the metadata of data set 1 of data provider A.

[0088] It should be noted here that in addition to the data fields in the metadata of the data provider's data set, the task configuration information can also include the data fields in the metadata of the data user's data set. That is, the privacy computing task can use the data of the data provider to implement the calculation, or it can use the data user's own data and the data provider's data to implement the calculation.

[0089] In this embodiment, the cloud platform sends the task configuration page corresponding to the task plan selected by the data user to the data user. The data user configures the task configuration page according to his or her own needs, thereby realizing the construction of privacy computing tasks through the interaction between the cloud platform and the data user, which can improve the flexibility of privacy computing task construction.

[0090] In an optional embodiment of the present application, before receiving a purchase request from a data user for a data set from a data provider, the following specific steps are further included:

[0091] Receive access requests from data users for published data sets;

[0092] In response to the access request, page information of the data product page is sent to the data user, and the page information includes metadata of the published data set.

[0093] Published data sets can be understood as data products released by data holders managed on the cloud platform. The released data products are used for browsing and trading by tenants on the cloud platform.

[0094] It is understandable that when publishing a data set, the data holder can read the metadata from the bound computing node, and can also add other descriptive information to the data set, such as application scenario introduction, product name, etc., and then publish it. When the cloud platform receives the data publishing request from the data holder, the metadata and other descriptive information of the data set will be published as a data product.

[0095] The data product page is the cloud platform's response to the data user's access request. It is used to display the data products released by various data holders on the cloud platform to the data user's users. Users can select data products for trading on the data product page to trigger purchase.

[0096] For example, see Figure 4 The data product page shown displays the data products released by various data holders on the cloud platform. In the display area of ​​each data product, the data holder information, data set name, application scenario items and metadata items of the data product are displayed.

[0097] In this embodiment, the metadata of the data set published by the data holder is managed through the cloud platform, thereby providing a secure data acquisition method for the data user.

[0098] In an optional embodiment of the present application, before the above-mentioned step 202, it also includes: sending an approval request containing the privacy computing task to the data provider, and then executing step 202 when an approval response is received from the data provider.

[0099] An approval request can be understood as a request from the data provider to review the privacy computing task before the cloud platform processes the privacy computing task. The approval request carries relevant information about the privacy computing task, such as relevant information about the data provider's data set, information about the data user, etc.

[0100] The approval response is the response returned by the data provider to the approval request, which is used to indicate that the approval is passed, that is, the cloud platform can process the privacy computing task based on the approval response.

[0101] In this embodiment, the cloud platform sends an approval request for the privacy computing task to the data provider, so that the privacy computing task is executed only after the data provider agrees, thereby further improving data security.

[0102] In an optional embodiment of the present application, before the above-mentioned step 202, it also includes: generating a binding request including identity verification information and tenant information of the data partner based on the node information sent by the data partner for binding the computing node, and sending the binding request to the computing node indicated by the node information, so that the computing node stores the identity verification information and tenant information, thereby completing the binding of the data partner and the computing node.

[0103] The node information can be understood as the address information of the computing node deployed in the local environment of the data partner, through which the computing node can be communicated. Exemplarily, the node information includes IP (Internet Protocol) address, port, supported communication protocol, etc.

[0104] The identity verification information can be understood as the authentication information generated by the cloud platform for the computing node for identity verification. A copy of this identity verification information will be saved on both the cloud platform and the computing node, and will be used to perform security authentication on the subtask when the cloud platform subsequently sends the subtask to the computing node through data cooperation.

[0105] For example, assuming that the identity verification information can be AK / SK (Access Key ID / Secret Access Key), the cloud platform uses SK to encrypt the content of the subtask and obtain a signature, and then sends the subtask to the computing node with AK and signature. When the computing node receives the subtask, it will first find the corresponding SK based on AK, use SK to encrypt the content of the subtask to obtain a signature, and then compare the signature sent by the cloud platform with the signature calculated by the computing node. If they are consistent, the identity authentication is passed and the computing node executes the subtask.

[0106] Tenant information can be understood as the user information of the data partner logging into the cloud platform.

[0107] Exemplarily, when the cloud platform receives node information of a computing node input by a user of a data partner, it generates a binding request for tenant information including identity verification information, and transmits the binding request to the computing node based on the node information to complete the binding of the data partner and the computing node.

[0108] In this embodiment, by using identity verification information to bind data partners to computing nodes, it can be ensured that data access and management permissions are strictly controlled.

[0109] Embodiment 3

[0110] Based on the above embodiments, Figure 5 is a flow chart of another data processing method according to an exemplary embodiment. Figure 1 The data user in the data processing system shown is the execution subject, such as Figure 5 As shown, the following steps 501 to 504 are included:

[0111] Step 501: Displaying a task configuration page according to the task plan selected by the user and the metadata of the data set provided by the data partner. The data partner includes the data user and the data provider. The user is a user of the data user.

[0112] Step 502: receiving task configuration information input by the user on the task configuration page, the task configuration information including data fields involved in calculation and metadata corresponding to the data fields;

[0113] Step 503: Send the task configuration information to the cloud platform, so that the cloud platform generates a privacy computing task according to the task configuration information and sends a subtask to the computing node bound to the participant to which the target data set belongs; wherein the subtask is a task decomposed from the privacy computing task, the target data set is the data set involved in the subtask, the computing node is a node locally deployed by the participant for executing the task, the computing node can access the original data of the target data set to execute the task, and the participant is the data user or data provider;

[0114] Step 504: Receive the execution result of the privacy computing task returned by the cloud platform. The execution result of the privacy computing task is obtained by the cloud platform based on the execution result of the subtask returned by the computing node.

[0115] The above-mentioned task plans can be understood as the types of tasks that can be constructed by the cloud platform for users. Different task plans have different data processing requirements. Data users can choose task plans according to their own needs to construct privacy computing tasks.

[0116] Data partners include all participants in data cooperative research and development. Data users can use the data sets provided by data providers to implement privacy computing tasks, or data users can combine the data sets provided by data providers with their own data sets to implement privacy computing tasks. Therefore, data partners include data users and at least one data provider.

[0117] The task configuration page can be understood as a page provided to the user of the data user for task configuration, which contains configuration items that require user input, relevant information of the data set of the data partner, etc.

[0118] The task configuration information can be understood as the data information configured by the user of the data user on the task configuration page, which includes the data fields involved in the calculation and the metadata corresponding to the data fields.

[0119] It should be noted here that in addition to the data fields in the metadata of the data provider's data set, the task configuration information can also include the data fields in the metadata of the data user's data set. That is, in addition to using the data provider's data, the privacy computing task can also use the data user's own data.

[0120] The present application embodiments and the above Figure 2 The embodiments are based on the same inventive concept, and the specific implementation of steps 501 to 504 can be parameterized as steps 201 to 203, which will not be repeated here.

[0121] By adopting the scheme of the embodiment of the present application, by deploying computing nodes in the local environment of the data and binding the data partners to the computing nodes, the data users among the data partners can configure the tasks according to the task scheme selected by the user and the metadata of the data set provided by the data partners, and complete the construction of the privacy computing tasks on the cloud platform. Since the computing nodes are nodes deployed locally by the data partners for executing tasks, the cloud platform sends the subtasks disassembled from the privacy computing tasks to the computing nodes bound to the corresponding participants to access the original data of the data set in the local environment for execution, and then the data users receive the execution results of the privacy computing tasks fed back by the cloud platform, so that the original data is always in the local environment and does not need to be transmitted to the cloud, thereby ensuring the privacy and compliance of the data. It can be seen that the present application realizes the processing of privacy computing tasks by combining the capabilities of the cloud platform and the locally deployed computing nodes, avoids uploading sensitive information to the cloud, reduces the risk of data leakage, and thus improves the availability and security of privacy data processing.

[0122] In an optional embodiment of the present application, the above step 501 displays a task configuration page according to the task solution selected by the user and the metadata of the data set provided by the data partner, and specifically includes the following steps:

[0123] Send access requests for published datasets to the cloud platform;

[0124] Display the data product page returned by the cloud platform, which contains the metadata of the published dataset;

[0125] Send a purchase request to the cloud platform based on the target dataset purchased by the user on the data product page;

[0126] The task configuration page is displayed based on the metadata of the target dataset and the task solution selected by the user.

[0127] It should be noted that the preferred implementation of this embodiment can refer to the relevant description in Embodiment 1, which will not be repeated here.

[0128] Regarding the above Figures 1 to 5 Based on the embodiment shown, Figure 6 FIG. 1 is a schematic diagram of a data processing application scenario according to an exemplary embodiment. The cloud platform includes a data transaction center and consoles of different users. The user's console can browse and trade data products through the data transaction center. Figure 6 As shown, the process of implementing data processing is as follows:

[0129] 1. Data publishing and listing: User B, as the data seller (i.e., publisher), prepares the dataset B to be published in the local environment and deploys computing node B, then configures the metadata of the locally stored dataset B in computing node B. User B uses the console client provided by the cloud platform to log in to console B. User B enters the node information of computing node B through the console client. Console B generates a binding request containing identity verification information and user B's tenant information based on the node information and transmits it to computing node B to bind computing node B to console B. In this way, user B's console B can read the metadata of dataset B from computing node B, and publish the data product to the data transaction center of the cloud platform based on the metadata of dataset B. At the same time, console B records the metadata of dataset B to the storage space.

[0130] 2. Data retrieval and screening: User A, as a data buyer, logs in to Console A using the console client. User A sends an access request for the published data set to the data trading center through Console A. User A's console client displays the data product page. User A browses the data product page to find the data product he needs, such as Dataset B.

[0131] 3. Purchase transaction: User A confirms the required data product and then makes the purchase operation. He submits the purchase request to the data transaction center through console A to complete the transaction process. After the transaction is completed, the data transaction center creates a transaction relationship for user A and user B, records the transaction information to manage data usage rights, and records the metadata of data set B to the storage space of console A. It should be noted that the storage space of console A also records the metadata of data set A published by user A.

[0132] 4. Privacy computing task construction: User A selects the task plan provided by Console A according to his own needs. Console A displays the task configuration page on the console client of User A based on the task plan selected by User A, the metadata of dataset A and the metadata of dataset B. User A enters the task configuration information on the task configuration page. Console A generates a privacy construction task based on the received task configuration information.

[0133] 5. Execute the task and obtain the result: Console A transmits the approval request of the privacy construction task to the console B of user B through the data transaction center. User B of console B agrees to the approval request. After receiving the approval response from console B, console A decomposes the privacy computing task and sends the subtasks to the computing nodes bound to the participants to which the data sets belong, including computing node B of user B and computing node A of user A. Finally, console A feeds back the execution results of the privacy computing task to the console client of user A based on the execution results returned by each computing node for user A to view.

[0134] Based on the above application scenarios, by deploying the console on the cloud platform and deploying the computing nodes for data processing in the user's local environment, and by binding the console to the computing nodes, the console is allowed to read the metadata managed on the computing nodes and purchase data products published by other users as needed. The console builds privacy computing tasks based on the metadata of its own local data and the metadata of the purchased data products, and sends the subtasks decomposed from the privacy computing tasks to the relevant computing nodes for execution. Therefore, although the console can build privacy computing tasks, the actual data processing work is completed on the computing nodes, which strengthens the security of data processing and data control, and can protect data privacy and security to the greatest extent.

[0135] Corresponding to the above-mentioned data processing method embodiment, the present application also provides an embodiment of a data processing device.

[0136] Figure 7 is a schematic diagram of a data processing device according to an exemplary embodiment, the device is used to perform the above Figure 2 The data processing method provided in the embodiment is as follows: Figure 7 As shown, the data processing device includes:

[0137] A task construction module 710 is used to construct a privacy computing task based on the metadata of the data set provided by the data partner; the data partner includes a data user and a data provider;

[0138] The task execution module 720 is used to send the subtasks involved in the target data set decomposed from the privacy computing task to the computing node bound to the participant to which the target data set belongs, according to the target data set involved in the subtasks; wherein the participant is the data user or the data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute the task;

[0139] The result feedback module 730 is used to send the execution result of the privacy computing task to the data user among the data partners according to the execution result of the subtask returned by the computing node.

[0140] In an optional implementation, the task construction module 710 is specifically used to receive a purchase request from the data user for the data set of the data provider; in response to the purchase request, construct a privacy computing task based on the metadata of the data set of the data partner.

[0141] In an optional implementation, the device further includes ( Figure 7 not shown):

[0142] The access processing module is used to receive an access request from the data user for the published data set before the task construction module 710 receives a purchase request from the data user for the data set of the data provider; in response to the access request, send page information of the data product page to the data user, the page information including metadata of the published data set.

[0143] In an optional implementation, the task construction module 710 is specifically used to construct a privacy computing task based on the metadata of the data set of the data partner. According to the task plan selected by the data user, the task configuration page is sent to the data user, and the page information includes the metadata of the data set of the data partner; the privacy computing task is generated according to the task configuration information returned by the data user; the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields.

[0144] In an optional implementation, the device further includes ( Figure 7 not shown):

[0145] The approval module is used to send an approval request containing the privacy computing task to the data provider before the task execution module 720 sends the subtask involved in the privacy computing task to the computing node bound to the participant to which the target data set belongs; and execute the steps of the task execution module 720 when an approval response is received from the data provider.

[0146] In an optional implementation, the device further includes ( Figure 7 not shown):

[0147] A binding node module is used to generate a binding request including identity verification information and tenant information of the data partner based on the node information for binding the computing node sent by the data partner before the task execution module 720 sends the subtask involved in the subtask decomposed from the privacy computing task to the computing node bound to the participant to which the target data set belongs; and send the binding request to the computing node indicated by the node information so that the computing node stores the identity verification information and the tenant information.

[0148] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0149] Figure 8 is a schematic diagram of a data processing device according to an exemplary embodiment, the device is used to perform the above Figure 5 The data processing method provided in the embodiment is as follows: Figure 8 As shown, the data processing device includes:

[0150] The task management module 810 is used to display a task configuration page according to the task plan selected by the user and the metadata of the data set provided by the data partner, wherein the data partner includes a data user and a data provider, and the user is a user of the data user; receive the task configuration information input by the user on the task configuration page, wherein the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields; send the task configuration information to the cloud platform, so that the cloud platform generates a privacy computing task according to the task configuration information and sends a subtask to the computing node bound to the participant to which the target data set belongs; wherein the subtask is a task decomposed from the privacy computing task, the target data set is the data set involved in the subtask, the computing node is a node locally deployed by the participant for executing tasks, the computing node can access the original data of the target data set to execute tasks, and the participant is the data user or the data provider;

[0151] The result acquisition module 820 is used to receive the execution result of the privacy computing task returned by the cloud platform. The execution result of the privacy computing task is obtained by the cloud platform based on the execution result of the subtask returned by the computing node.

[0152] In an optional implementation, the task management module 810 is specifically used to send an access request for a published data set to the cloud platform during the process of displaying the task configuration page based on the task plan selected by the user and the metadata of the data set provided by the data partner; display the data product page returned by the cloud platform, the data product page including the metadata of the published data set; send a purchase request to the cloud platform based on the target data set purchased by the user on the data product page; and display the task configuration page based on the metadata of the target data set and the task plan selected by the user.

[0153] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0154] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0155] In an embodiment of the present application, computing nodes are deployed in the local environment of the data and the data partners are bound to the computing nodes. Thus, the metadata of the data set provided by the data partners can be used to build a privacy computing task on the cloud. Since the computing nodes are nodes deployed locally by the data partners for executing tasks, the subtasks decomposed from the privacy computing task are sent to the computing nodes bound to the data partners for execution. Then, the execution results of the privacy computing task are fed back to the data users based on the execution results of the subtasks returned by the computing nodes, thereby completing the required data calculation and processing. This ensures that the original data is always in the local environment and does not need to be transmitted to the cloud, thereby ensuring the privacy and compliance of the data.

[0156] The embodiment of the present application also provides an electronic device corresponding to the data processing method provided in the above embodiment to execute the above data processing method.

[0157] Fig. 96 is a hardware structure diagram of an electronic device according to an exemplary embodiment, the electronic device includes: a communication interface 601, a processor 602, a memory 603 and a bus 604; wherein the communication interface 601, the processor 602 and the memory 603 communicate with each other through the bus 604. The processor 602 can execute the data processing method described above by reading and executing the machine executable instructions corresponding to the control logic of the data processing method in the memory 603. The specific content of the method is referred to the above embodiment, which will not be repeated here.

[0158] The memory 603 mentioned in this application can be any electronic, magnetic, optical or other physical storage system, and can contain storage information, such as executable instructions, data, etc. Specifically, the memory 603 can be RAM (Random Access Memory), flash memory, storage drive (such as hard disk drive), any type of storage disk (such as optical disk, DVD, etc.), or similar storage medium, or a combination thereof. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 601 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0159] The bus 604 may be an ISA bus, a PCI bus or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 603 is used to store programs, and the processor 602 executes the programs after receiving execution instructions.

[0160] Processor 602 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in processor 602 or an instruction in software form. The above-mentioned processor 602 can be a general-purpose processor, including a network processor (Network Processor, referred to as NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute.

[0161] The electronic device provided in the embodiment of the present application and the data processing method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented therein.

[0162] The present application also provides a computer-readable storage medium corresponding to the data processing method provided in the above embodiment. Fig.10 As shown, the computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the data processing method provided by any of the aforementioned embodiments will be executed.

[0163] It should be noted that examples of the computer-readable storage medium may also 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 optical or magnetic storage media, which are not listed here one by one.

[0164] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the data processing method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0165] An embodiment of the present application also provides a computer program product corresponding to the data processing method provided in the aforementioned embodiment. The computer program product includes a computer program, and the computer program is executed by a processor to implement the data processing method provided in the aforementioned embodiment.

[0166] The computer program product provided by the above-mentioned embodiments of the present application and the data processing method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0167] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0168] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0169] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: Applied to a cloud platform, the method includes: Constructing a privacy computing task based on the metadata of the data set provided by the data partners; the data partners include data users and data providers; According to the target data set involved in the subtasks decomposed from the privacy computing task, the subtask is sent to the computing node bound to the participant to which the target data set belongs; wherein the participant is the data user or the data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute tasks, and the original data is always in the local environment and does not need to be transmitted to the cloud platform; According to the execution result of the subtask returned by the computing node, sending the execution result of the privacy computing task to the data user; The step of constructing a privacy computing task based on the metadata of the data set of the data partner includes: According to the task scheme selected by the data user, page information of a task configuration page is sent to the data user, wherein the page information includes metadata of the data set of the data partner, and the metadata includes data fields of tables in the data set and data types of the data fields; The privacy computing task is generated according to the task configuration information returned by the data user; the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields.

2. The method according to claim 1, characterized in that The privacy computing task is constructed based on the metadata of the data set provided by the data partner, including: receiving a purchase request from the data user for the data set of the data provider; In response to the purchase request, a privacy computing task is constructed according to the metadata of the data set of the data partner.

3. The method according to claim 2, characterized in that Before receiving a purchase request from the data user for the data set of the data provider, the method further includes: receiving a request from the data user for access to the published data set; In response to the access request, page information of the data product page is sent to the data user, the page information including metadata of the published data set.

4. The method according to any one of claims 1 to 3, characterized in that: Before sending the target data set involved in the subtask decomposed from the privacy computing task to the computing node bound to the participant to which the target data set belongs, the method further includes: Sending an approval request including the privacy computing task to the data provider; Upon receiving an approval response from the data provider, execute the target data set involved in the subtasks decomposed from the privacy computing task, and send the subtask to the computing node bound to the participant to which the target data set belongs.

5. The method according to any one of claims 1 to 3, characterized in that: Before sending the target data set involved in the subtask decomposed from the privacy computing task to the computing node bound to the participant to which the target data set belongs, the method further includes: Generate a binding request including identity verification information and tenant information of the data partner according to the node information for binding a computing node sent by the data partner; The binding request is sent to the computing node indicated by the node information, so that the computing node stores the identity verification information and the tenant information.

6. A data processing method, characterized in that: The method comprises: Displaying a task configuration page according to the task solution selected by the user and the metadata of the data set provided by the data partner; the data partner includes a data user and a data provider, the user is a user of the data user, and the metadata includes the data fields of the tables in the data set and the data types of the data fields; Receiving task configuration information input by the user on the task configuration page, the task configuration information including data fields involved in calculation and metadata corresponding to the data fields; Send the task configuration information to the cloud platform, so that the cloud platform generates a privacy computing task according to the task configuration information and sends a subtask to the computing node bound to the participant to which the target data set belongs; wherein the subtask is a task decomposed according to the privacy computing task, the target data set is the data set involved in the subtask, the computing node is a node locally deployed by the participant for executing tasks, the computing node can access the original data of the target data set to execute tasks, the original data is always in the local environment and does not need to be transmitted to the cloud platform, and the participant is the data user or the data provider; Receive the execution result of the privacy computing task returned by the cloud platform, where the execution result of the privacy computing task is obtained by the cloud platform based on the execution result of the subtask returned by the computing node.

7. The method according to claim 6, characterized in that The task configuration page is displayed based on the task solution selected by the user and the metadata of the data set provided by the data partner, including: Send access requests for published datasets to the cloud platform; Displaying a data product page returned by the cloud platform, wherein the data product page includes metadata of the published data set; Sending a purchase request to the cloud platform according to the target data set purchased by the user on the data product page; A task configuration page is displayed according to the metadata of the target data set and the task solution selected by the user.

8. A data processing system, characterized in that: The system includes a cloud platform and data partners; the data partners include data users and data providers, the data providers and the data users are both bound to computing nodes, and the computing nodes are nodes locally deployed by the data partners for executing tasks; The data user is used to display a task configuration page according to the task solution selected by the user and the metadata of the data set provided by the data partner, wherein the metadata includes the data fields of the table in the data set and the data types of the data fields; receive the task configuration information input by the user on the task configuration page; send the task configuration information to the cloud platform, wherein the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields; The cloud platform is used to generate a privacy computing task according to the task configuration information, and send the subtask to the computing node bound to the participant to which the target data set belongs according to the target data set involved in the subtask decomposed from the privacy computing task, and send the execution result of the privacy computing task to the data user according to the execution result of the subtask returned by the computing node bound to the participant; the participant is the data user or the data provider, and the computing node bound to the participant can access the original data of the target data set to execute the task, and the original data is always in the local environment and does not need to be transmitted to the cloud platform.

9. A data processing device, characterized in that: Applied to a cloud platform, the device comprises: A task construction module, used to construct a privacy computing task based on the metadata of the data set provided by the data partner; the data partner includes the data user and the data provider; A task execution module, used to send the subtasks involved in the target data set decomposed from the privacy computing task to the computing node bound to the participant to which the target data set belongs; wherein the participant is the data user or the data provider, and the computing node is a node locally deployed by the participant for executing tasks, and the computing node can access the original data of the target data set to execute tasks, and the original data is always in the local environment and does not need to be transmitted to the cloud platform; A result feedback module, used to send the execution result of the privacy computing task to the data user according to the execution result of the subtask returned by the computing node; Among them, the task construction module is specifically used to send page information of the task configuration page to the data user according to the task plan selected by the data user, and the page information includes the metadata of the data set of the data partner, and the metadata includes the data fields of the tables in the data set and the data types of the data fields; generate the privacy computing task according to the task configuration information returned by the data user; the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields.

10. A data processing device, characterized in that: The device comprises: A task management module is used to display a task configuration page according to the task plan selected by the user and the metadata of the data set provided by the data partner, wherein the data partner includes a data user and a data provider, the user is a user of the data user, and the metadata includes the data fields of the table in the data set and the data type of the data field; receive the task configuration information entered by the user on the task configuration page, the task configuration information includes the data fields involved in the calculation and the metadata corresponding to the data fields; send the task configuration information to the cloud platform, so that the cloud platform generates a privacy computing task according to the task configuration information and sends a subtask to the computing node bound to the participant to which the target data set belongs; wherein the subtask is a task decomposed from the privacy computing task, the target data set is the data set involved in the subtask, the computing node is a node locally deployed by the participant for executing tasks, the computing node can access the original data of the target data set to execute tasks, the original data is always in the local environment and does not need to be transmitted to the cloud platform, and the participant is the data user or the data provider; The result acquisition module is used to receive the execution result of the privacy computing task returned by the cloud platform, and the execution result of the privacy computing task is obtained by the cloud platform according to the execution result of the subtask returned by the computing node.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes the program to implement the method according to any one of claims 1 to 7.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 7.

13. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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