Data processing method and system based on privacy calculation

Abstract

The application provides a data processing method and system based on privacy calculation. A server selects first operation data corresponding to each task request from a database according to a task request, classifies the first operation data to obtain a plurality of second operation data, and generates a data classification table according to the correspondence between the second operation data and an initiating end. The second operation data is distributed according to the residual occupation of the CPU of each blockchain node, so that each blockchain node has a corresponding privacy calculation target. After the blockchain node performs privacy calculation on the distributed second operation data, third operation data is obtained and sent to the server. After judging that all blockchain nodes respectively feed back the third operation data, the server merges the third operation data according to the data classification table, obtains fourth operation data, and sends the fourth operation data to the corresponding initiating end, thereby improving the efficiency of privacy calculation.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to a data processing method and system based on privacy computing. Background Technology

[0002] Privacy computing refers to a set of technologies that enable data analysis and computation while protecting the data itself from being disclosed to the outside world, achieving the goal of making the data "usable but not visible"; and realizing the transformation and release of data value while fully protecting data and privacy security.

[0003] Currently, participants in privacy-preserving computation are typically divided into three categories: initiators, servers, and initiators. Initiators are generally data requesters who send corresponding task requests. Servers are generally data providers who respond to the initiators' task requests by sending the corresponding data to the server for computation. The server then sends the computation results back to the initiator. Existing technologies can also eliminate the server to avoid privacy leaks and establish communication between the initiator and the server. However, multiple initiators may send multiple identical or similar task requests to the server, resulting in redundant and repetitive computations by the server, wasting computing resources and slowing down task processing efficiency. Summary of the Invention

[0004] This invention provides a data processing method and system based on privacy computing. By deduplicating data through server-side packaging, the computational load on the server is reduced, thereby improving data processing efficiency.

[0005] A first aspect of this invention provides a data processing method based on privacy computing, comprising:

[0006] Multiple initiators send task requests. The server selects the first computation data corresponding to each task request from the database according to the task request, classifies the first computation data to obtain multiple second computation data, and generates a data classification table according to the correspondence between the second computation data and the initiator.

[0007] The server distributes the second computational data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation objective;

[0008] After performing privacy calculations on the distributed second computational data, the blockchain node obtains the third computational data and sends the third computational data to the server.

[0009] After determining that all blockchain nodes have returned the third operation data, the server merges the third operation data according to the data classification table to obtain the fourth operation data and sends it to the corresponding initiator.

[0010] Optionally, in one possible implementation of the first aspect, the plurality of initiating ends send task requests, the server selects first computational data corresponding to each task request from the database according to the task requests, classifies the first computational data to obtain a plurality of second computational data, and generates a data classification table according to the correspondence between the second computational data and the initiating ends, including:

[0011] Multiple initiators send task requests, and the server selects the first computation data corresponding to each task request from the database according to the task requests;

[0012] The intersection of the first operation data corresponding to multiple initiators is calculated, and the intersection of the data of any two initiators is obtained as the first intersection data.

[0013] Compare the first computational data of each initiating end with its corresponding first intersection data to obtain the first difference data of the corresponding initiating end;

[0014] Select the first intersection data and / or first difference data corresponding to each initiating end to generate a data classification table. Each initiating end corresponds to at least one first intersection data and / or first difference data. Use the first intersection data and / or first difference data as the second operation data.

[0015] Optionally, in one possible implementation of the first aspect, the step of performing intersection calculations on the first computational data corresponding to multiple initiating ends to determine the first intersection data by the intersection of data from any two initiating ends includes:

[0016] Obtain the first dimension information of the first operation data corresponding to multiple initiating ends, decompose the first operation data according to the first dimension information to obtain at least one operation sub-data, and determine the first dimension label of each operation sub-data;

[0017] Compare the first dimension labels of any two initiating sub-data, and select the sub-data with the same first dimension label as the first intersection data.

[0018] Optionally, in one possible implementation of the first aspect, the step of performing intersection calculations on the first computational data corresponding to multiple initiating ends to determine the first intersection data by the intersection of data from any two initiating ends includes:

[0019] Obtain first dimension information of first operation data corresponding to multiple initiating ends. If there are multiple first dimension information, decompose the first operation data according to the multiple first dimension information to obtain at least one operation sub-data.

[0020] Determine the label group corresponding to each operational sub-data, wherein the label group includes first dimension information of multiple corresponding categories;

[0021] Compare the label groups of any two initiating sub-data, and select the sub-data with the same label group as the first intersection data.

[0022] Optionally, in one possible implementation of the first aspect, the step of selecting the first intersection data and / or the first difference data corresponding to each initiating end, generating a data classification table, wherein each initiating end corresponds to at least one first intersection data and / or the first difference data, and using the first intersection data and / or the first difference data as the second operation data, includes:

[0023] Initialize the data classification table, and create a first column and a second column in the data classification table. The first cell in each first column has a corresponding relationship with the second cell in the second column.

[0024] Enter the ID of each initiator into the first cell of the first column, extract the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and enter the first dimension label and / or label group into the corresponding second cell to generate a data classification table.

[0025] Optionally, in one possible implementation of the first aspect, the step of filling the ID of each initiator into the first cell of the first column, extracting the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and filling the first dimension label and / or label group into the corresponding second cell to generate a data classification table includes:

[0026] Obtain the metadata of all operational sub-data within the first intersection data, and generate a first metadata sub-column based on the metadata of the first intersection data;

[0027] Obtain the metadata of all operational sub-data within the first difference set data, and generate a second metadata sub-column based on the metadata of the first difference set data;

[0028] A third cell is created within the data classification table. Each third cell has a corresponding second cell. The first metadata sub-column and / or the second metadata sub-column are filled into the third cell corresponding to the first dimension label and / or label group.

[0029] Optionally, in one possible implementation of the first aspect, the server distributes the second computational data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation objective, including:

[0030] Obtain the first metadata sub-column and / or the second metadata sub-column corresponding to the first intersection data and / or the first difference data for each second operation data;

[0031] The number of metadata corresponding to each second operation data is obtained by summing the number of metadata in the first metadata sub-column and / or the second metadata sub-column corresponding to each second operation data.

[0032] Sort all the second operation data in descending order according to the amount of the metadata to obtain a quantity sequence;

[0033] The server sorts each blockchain node in descending order based on the remaining CPU usage of each blockchain node, and generates a computation sequence.

[0034] If the number of second operation data in the quantity sequence is less than or equal to the number of blockchain nodes in the operation sequence, then the second operation data is distributed to the corresponding blockchain nodes in the operation sequence in sequence according to the quantity sequence, so that each blockchain node corresponds to at most one second operation data.

[0035] If the number of second operation data in the quantity sequence is greater than the number of blockchain nodes in the operation sequence, then all the second operation data are distributed cyclically to the corresponding blockchain nodes in the operation sequence according to the quantity sequence, so that each blockchain node corresponds to at least one second operation data.

[0036] Optionally, in one possible implementation of the first aspect, after the server determines that all blockchain nodes have respectively returned the third computation data, it merges the third computation data according to the data classification table to obtain the fourth computation data and sends it to the corresponding initiator, including:

[0037] If it is determined that all blockchain nodes have fed back the third computation data, then select the first dimension label and / or label group in the second cell corresponding to the ID of the initiator in the data classification table;

[0038] Based on the first dimension label and / or label group, select the corresponding third operation data, copy and merge the selected third operation data to obtain the fourth operation data;

[0039] The fourth operation data is sent to the initiating end with the corresponding ID.

[0040] Optionally, in one possible implementation of the first aspect, it also includes:

[0041] If the initiating end receives the corresponding fourth calculation data, it generates feedback information and sends the feedback information to the server.

[0042] The server formats the fourth operation data of the corresponding ID based on the feedback information.

[0043] A second aspect of the present invention provides a data processing system based on privacy computing, comprising:

[0044] The classification module is used for multiple initiating ends to send task requests. The server selects the first operation data corresponding to each task request from the database according to the task request, classifies the first operation data to obtain multiple second operation data, and generates a data classification table according to the correspondence between the second operation data and the initiating ends.

[0045] The distribution module is used by the server to distribute the second computation data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation target.

[0046] The computing module is used by blockchain nodes to obtain third computing data after performing privacy calculations on the distributed second computing data, and then send the third computing data to the server.

[0047] The merging module is used by the server to merge the third operation data according to the data classification table after all blockchain nodes have returned the third operation data, to obtain the fourth operation data, and then send it to the corresponding initiator.

[0048] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program, wherein the computer program is stored in the memory, and the processor executes the computer program to perform the methods described in the first aspect of the present invention and various possible methods related to the first aspect.

[0049] A fourth aspect of the present invention provides a readable storage medium storing a computer program, which, when executed by a processor, is used to implement the first aspect of the present invention and various methods possibly involved in the first aspect.

[0050] This invention provides a data processing method and system based on privacy computation. By classifying the first computational data, when duplicate data exists, only one data is selected as the corresponding second computational data. This invention determines the corresponding data classification table based on the different second computational data corresponding to the initiating end, so that when assembling the privacy-computed third computational data, it can be assembled according to the data classification table to obtain the fourth computational data required by each initiating end. This method effectively reduces the privacy computation workload of duplicate data, so that the privacy data required by different initiating ends only needs to be calculated once during the privacy computation process. While reducing the privacy computation workload, it also significantly improves the computation workload of subsequent data for multiple initiating ends in high-concurrency scenarios. This solution matches the size of the second computational data with the remaining CPU usage of the blockchain node, so that the node does not operate under overload. Data is reasonably allocated to each block node according to its computing power, improving the privacy computation efficiency of the entire system for all second computational data.

[0051] The technical solution provided by this invention performs intersection and difference operations on the first set of computational data, enabling the system to select only one record for processing when there are duplicate data. By filling the ID of each initiator into the first cell of the data classification table and filling the first dimension label and / or label group into the corresponding second cell, each initiator has its own corresponding first dimension label and / or label group, generating a data classification table. This facilitates the subsequent copying and merging of data based on the first dimension label and / or label group corresponding to the third set of computational data, thereby improving computational efficiency while ensuring the accuracy of the data required by the initiator.

[0052] The technical solution provided by this invention sorts the first metadata sub-column and the second metadata sub-column corresponding to the first intersection data and the first difference data in the second operation data in descending order according to the amount of metadata. At the same time, it sorts the remaining CPU usage of the blockchain nodes at the server in descending order. The more metadata there is, the larger the data volume is. Therefore, nodes with more metadata are matched with nodes with more remaining CPU usage, and nodes with less metadata are matched with nodes with less remaining CPU usage. Compared with traditional technology, which requires statistical calculation of data volume and then matching with nodes, this invention does not need to statistically calculate the data volume and directly allocates based on the amount of metadata, avoiding the waste of computing power and improving computing efficiency. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the application scenario of the technical solution provided by the present invention;

[0054] Figure 2 A flowchart of a privacy-based computation-based data processing method provided by the present invention;

[0055] Figure 3 This is a schematic diagram of the structure of a privacy-based computing-based data processing system provided by the present invention;

[0056] Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided by the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention 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 so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0059] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0060] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0061] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0062] It should be understood that in this invention, "B corresponding to A", "B corresponding to A", "A and B correspond", or "B and A correspond" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0063] Depending on the context, "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection."

[0064] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0065] like Figure 1 The diagram illustrates a scenario of the technical solution provided by this invention, including: an initiating end and a server end. Multiple initiating ends send task requests to the server end, where a task request can be one or multiple, without limitation. The server end responds to the task requests and extracts the corresponding first computational data from the database according to the task requests. It then classifies and packages the first computational data to obtain second computational data and a corresponding data classification table. The data is sorted in descending order according to the amount of metadata corresponding to each second computational data to generate a quantity sequence. The remaining CPU usage of each blockchain node is also sorted in descending order to generate a computation sequence. The second computational data is distributed to the corresponding blockchain nodes in the computation sequence according to the quantity sequence for computation to obtain third computational data. Subsequently, the third computational data is merged according to the data classification table to obtain fourth computational data, and the fourth computational data is sent to the corresponding initiating end.

[0066] This invention provides a data processing method based on privacy computing, such as... Figure 2 As shown, it includes:

[0067] Step S110: Multiple initiating ends send task requests. The server selects the first operation data corresponding to each task request from the database according to the task request, classifies the first operation data to obtain multiple second operation data, and generates a data classification table according to the correspondence between the second operation data and the initiating ends.

[0068] The technical solution provided by this invention involves multiple initiating ends sending task requests to a server. The server selects corresponding first operation data from the database based on the task requests. The task requests can be one or multiple, without limitation. The first operation data is selected from the database before removing duplicate data. Furthermore, the first operation data is categorized to obtain multiple second operation data. Essentially, the first operation data is split and categorized into duplicate and non-duplicate data, both of which are considered second operation data. A data classification table is generated based on the correspondence between the second operation data and the initiating ends.

[0069] For example, Douyin and Kuaishou send task requests to Taobao to investigate popular product categories. Douyin's task request is to investigate popular subcategories of food from May to August, and Kuaishou's task request is to investigate popular subcategories of food from July to October. Taobao selects the corresponding first-stage data from its database based on the task requests from Douyin and Kuaishou. At this point, based on the popular subcategories of food from May to August on Douyin and the popular subcategories of food from July to October on Kuaishou, it can be seen that the first-stage data corresponds to the popular subcategories of food from July to August. Therefore, the data is divided into: popular subcategories of food from May to June, popular subcategories of food from July to August, and popular subcategories of food from September to October. The first-stage data is classified into multiple second-stage data. It can be understood that the first-stage data is divided into duplicate data and non-duplicate data to obtain the second-stage data. A data classification table is generated based on the correspondence between the second-stage data and Douyin and Kuaishou.

[0070] This invention reduces the amount of data processing and improves task processing efficiency by classifying the first calculation data and removing duplicate data requirements from multiple initiators. It also generates a data classification table based on the correspondence between the second calculation data and the initiators to facilitate the subsequent merging of calculation results.

[0071] In one possible implementation of the technical solution provided by the present invention, step S110 specifically includes:

[0072] Multiple initiators send task requests, and the server selects the first computation data corresponding to each task request from the database based on the task requests.

[0073] The technical solution provided by this invention involves multiple initiating ends sending task requests. It is understood that the task request corresponding to an initiating end can be one or multiple, and this is not limited here. The server selects the first calculation data corresponding to each task request from the database according to the task request, so as to facilitate subsequent calculation on the first calculation data to obtain the result corresponding to each task request.

[0074] For example, Douyin sends a task request to Taobao to investigate popular product categories. Douyin's task request is to investigate popular subcategories of food from May to August. Taobao selects the data of popular subcategories of food from May to August from the database corresponding to the task request, and selects the data corresponding to the task from the database to ensure the accuracy of the data calculation results.

[0075] The intersection of the first operation data corresponding to multiple initiating ends is calculated, and the intersection of the data of any two initiating ends is obtained as the first intersection data.

[0076] The technical solution provided by this invention performs intersection calculation on the first operation data corresponding to multiple initiating ends, and extracts the intersection of data from any two initiating ends to obtain the first intersection data. It can be understood that the first operation data of the intersection part of multiple first operation data is taken as the first intersection data. By performing intersection calculation on the first operation data, it is convenient to remove duplicate data in the subsequent process, thereby reducing the amount of data calculation.

[0077] For example, the first data for Douyin is the popular subcategories of food from May to August, and the first data for Kuaishou is the popular subcategories of food from July to October. By performing an intersection operation on the two, the first intersection data is the popular subcategories of food from July to August.

[0078] In one possible implementation of the technical solution provided by this invention, the step of performing intersection calculations on the first computational data corresponding to multiple initiating ends to determine the intersection of data from any two initiating ends to obtain the first intersection data includes:

[0079] Obtain the first dimension information of the first operation data corresponding to multiple initiating ends, decompose the first operation data according to the first dimension information to obtain at least one operation sub-data, and determine the first dimension label of each operation sub-data.

[0080] The technical solution provided by this invention involves obtaining first-dimensional information of first computational data corresponding to multiple initiating ends. The first-dimensional information can be time information or category information, which is not limited here. The first computational data is decomposed according to the first-dimensional information to obtain multiple computational sub-data. The first-dimensional label corresponding to each computational sub-data is determined to facilitate subsequent classification processing of the first computational data according to the first-dimensional label.

[0081] For example: Obtain the time information of popular food subcategories on Douyin from May to August, and decompose the first calculation data into May, June, July, and August sub-data based on the time information. Determine the first dimension label of each sub-data as: May, June, July, and August. Obtain the time information of popular food subcategories on Kuaishou from July to October, and decompose the first calculation data into July, August, September, and October sub-data based on the time information. Determine the first dimension label of each sub-data as: July, August, September, and October.

[0082] Compare the first dimension labels of any two initiating sub-data, and select the sub-data with the same first dimension label as the first intersection data.

[0083] The technical solution provided by this invention compares the first dimension labels of the operational sub-data of any two initiating ends one by one, and selects the operational sub-data with the same first dimension label as the first intersection data. It can be understood that the data with the same label is selected as the first intersection data.

[0084] For example, by comparing the first-dimensional tags for Douyin (May, June, July, August) and Kuaishou (July, August, September, October), the sub-data with the same first-dimensional tags are the sub-data for July to August. In this case, the first intersection data is the sub-data for July to August, which can also be called the first operation data for July to August, making it convenient to remove duplicates based on the first intersection data later.

[0085] In one possible implementation of the technical solution provided by this invention, the step of performing intersection calculations on the first computational data corresponding to multiple initiating ends to determine the intersection of data from any two initiating ends to obtain the first intersection data includes:

[0086] Obtain the first dimension information of the first operation data corresponding to multiple initiating ends. If there are multiple first dimension information, decompose the first operation data according to the multiple first dimension information to obtain at least one operation sub-data.

[0087] The technical solution provided by the present invention obtains the first dimension information of the first operation data corresponding to multiple initiating ends. If there are multiple first dimension information, it can be understood that the first dimension information can be time information and category information, which is not limited here. Then, the first operation data is decomposed according to the multiple first dimension information to obtain at least one operation sub-data.

[0088] For example, Douyin's task request is to investigate popular subcategories of food and daily necessities from May to August, while Kuaishou's task request is to investigate popular subcategories of food and furniture from July to October. In this case, Douyin's first calculation data would be the popular subcategories of food and daily necessities from May to August, and Kuaishou's would be the popular subcategories of food and furniture from July to October. The first dimension information in this case would be category information and time information. Therefore, the first calculation data would be decomposed based on the category information and time information. The sub-data for the calculations are: May food calculation data, June food calculation data, July food calculation data, August food calculation data, May daily necessities calculation data, June daily necessities calculation data, July daily necessities calculation data, August daily necessities calculation data; the sub-data for Douyin are: July food calculation data, August food calculation data, September food calculation data, October food calculation data, July furniture calculation data, August furniture calculation data, September furniture calculation data, October furniture calculation data.

[0089] Determine the label group corresponding to each operational sub-data, wherein the label group includes first dimension information of multiple categories.

[0090] The technical solution provided by this invention determines the label group corresponding to each operational sub-data. It can be understood that at this time, the label corresponding to each operational sub-data is multiple, which are called label groups. The label group includes first dimension information of multiple categories. The first dimension information can be time information and category information, which are not limited here. This facilitates the subsequent determination of the same operational sub-data based on the label group to obtain the first intersection data.

[0091] For example, the tag groups corresponding to each sub-data operation on Douyin are: (May, food), (June, food), (July, food), (August, food), (May, daily necessities), (June, daily necessities), (July, daily necessities), (August, daily necessities); the tag groups corresponding to each sub-data operation on Kuaishou are: (July, food), (August, food), (September, food), (October, food), (July, furniture), (August, furniture), (September, furniture), (October, furniture).

[0092] Compare the label groups of any two initiating sub-data, and select the sub-data with the same label group as the first intersection data.

[0093] The technical solution provided by this invention compares the tag groups of the operational sub-data from any two initiating ends, and selects the operational sub-data with the same tag group as the first intersection data. It can be understood that selecting the data with the same tag group as the first intersection data and calculating the intersection data through the tag group achieves the deduplication of the data.

[0094] For example, compare the tag groups corresponding to Douyin: (May, food), (June, food), (July, food), (August, food), (May, daily necessities), (June, daily necessities), (July, daily necessities), (August, daily necessities) with the tag groups corresponding to Kuaishou: (July, food), (August, food), (September, food), (October, food), (July, furniture), (August, furniture), (September, furniture), (October, furniture), and select the sub-data of (July, food) and (August, food) as the first intersection data.

[0095] The first operation data of each initiator is compared with its corresponding first intersection data to obtain the first difference data of the corresponding initiator.

[0096] The technical solution provided by this invention performs a difference operation on the first operation data of each initiating end and its corresponding first intersection data to obtain the corresponding first difference data. The first operation data is classified through the intersection and difference operations, thereby removing duplicate data and reducing the amount of computation for subsequent data.

[0097] For example: The first intersection data is the sub-data of (July, food) and (August, food), which corresponds to the popular subcategories of food from July to August. Taking the difference between the popular subcategories of food and daily necessities from Douyin from May to August and the popular subcategories of food from July to August, the first difference set data is: the popular subcategories of food from May to June and the popular subcategories of daily necessities from May to August. Taking the difference between the popular subcategories of food and furniture from Kuaishou from July to October and the popular subcategories of food from July to August, the first difference set data is: the popular subcategories of food from September to October and the popular subcategories of furniture from July to October.

[0098] Select the first intersection data and / or first difference data corresponding to each initiating end to generate a data classification table. Each initiating end corresponds to at least one first intersection data and / or first difference data. Use the first intersection data and / or first difference data as the second operation data.

[0099] The technical solution provided by this invention generates a corresponding data classification table based on the first intersection data and / or first difference data corresponding to each initiating end. Each initiating end corresponds to at least one first intersection data and / or first difference data. It can be understood that the first intersection data can be an empty set or all the first operation data, i.e., itself. Therefore, the corresponding difference set can be all the first operation data or an empty set. The first intersection data and / or first difference data are used as the second operation data. The data classification table is generated through the correspondence between the first intersection data and / or first difference data and the initiating end to facilitate subsequent data merging based on the data classification table and sending the calculation results to the corresponding initiating end.

[0100] In one possible implementation of the technical solution provided by this invention, the step of selecting the first intersection data and / or the first difference data corresponding to each initiating end, generating a data classification table, wherein each initiating end corresponds to at least one first intersection data and / or the first difference data, and using the first intersection data and / or the first difference data as the second operation data includes:

[0101] Initialize the data classification table, and create a first column and a second column in the data classification table. The first cell in each first column has a corresponding relationship with the second cell in the second column.

[0102] The technical solution provided by this invention initializes a data classification table, establishes a first column and a second column in the data classification table, establishes multiple first cells in the first column, and establishes multiple second cells in the second column, with each second cell having a corresponding first cell.

[0103] Enter the ID of each initiator into the first cell of the first column, extract the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and enter the first dimension label and / or label group into the corresponding second cell to generate a data classification table.

[0104] The technical solution provided by this invention fills the ID of each initiator into the first cell of the first column, and extracts the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and fills the first dimension label and / or label group into the corresponding second cell. It can be understood that at this time, the ID of the initiator in the first cell corresponds to the first dimension label and / or label group in the second cell. After the data is filled, the corresponding data classification table is generated.

[0105] In one possible implementation of the technical solution provided by this invention, the step of filling the ID of each initiator into the first cell of the first column, extracting the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and filling the first dimension label and / or label group into the corresponding second cell to generate a data classification table includes:

[0106] Obtain the metadata of all operational sub-data within the first intersection data, and generate a first metadata sub-column based on the metadata of the first intersection data.

[0107] The technical solution provided by this invention obtains the metadata of all operational sub-data within the first intersection data. It can be understood that each operational sub-data has corresponding metadata, which is also called intermediary data. Metadata is data that describes data and is used to find the corresponding data. A first metadata sub-column is generated based on the metadata of the first intersection data.

[0108] Obtain the metadata of all operational sub-data within the first difference set data, and generate a second metadata sub-column based on the metadata of the first difference set data.

[0109] The technical solution provided by this invention obtains the metadata of all operational sub-data within the first difference set data. It can be understood that each operational sub-data has corresponding metadata, which is also called intermediary data. Metadata is data that describes data and is used to find the corresponding data. A second metadata sub-column is generated based on the metadata of the first difference set data.

[0110] A third cell is created within the data classification table. Each third cell has a corresponding second cell. The first metadata sub-column and / or the second metadata sub-column are filled into the third cell corresponding to the first dimension label and / or label group.

[0111] The technical solution provided by this invention establishes a third column in the data classification table, and a third cell in the third column. Each third cell has a corresponding second cell, and the second cell also has a corresponding first cell. The first metadata sub-column and / or the second metadata sub-column are filled into the third cell corresponding to the first dimension label and / or label group. This facilitates the subsequent determination of the corresponding data volume based on the amount of metadata in the metadata sub-column, and can more accurately match the corresponding blockchain node for calculation, resulting in higher calculation efficiency.

[0112] Step S120: The server distributes the second computation data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation target.

[0113] The technical solution provided by this invention distributes the second computational data to the server based on the remaining CPU usage of each blockchain node. It can be understood that the larger the remaining CPU usage of a blockchain node, the higher the relative remaining computing power, which makes the CPU load of the blockchain node moderate, extends its service life, and improves the computing efficiency, so that the allocated blockchain nodes have corresponding privacy computing objectives.

[0114] In one possible implementation of the technical solution provided by the present invention, step S120 specifically includes:

[0115] Obtain the first metadata sub-column and / or the second metadata sub-column corresponding to the first intersection data and / or the first difference data for each second operation data.

[0116] The technical solution provided by this invention involves the system acquiring a first metadata sub-column and / or a second metadata sub-column corresponding to the first intersection data and / or the first difference data for each second operation data. It can be understood that the first intersection data can be an empty set, and the first difference data can be an empty set. Therefore, the corresponding metadata sub-column can only contain the first metadata sub-column or the second metadata sub-column. When the first intersection data and the first difference data are not empty sets, both the first metadata sub-column and the second metadata sub-column exist simultaneously.

[0117] The total number of metadata entries for each second operation data is calculated by summing the number of metadata entries in the first metadata sub-column and / or the second metadata sub-column corresponding to that second operation data.

[0118] The technical solution provided by this invention involves summing the number of metadata in the first metadata sub-column and / or the second metadata sub-column corresponding to each second operational data to obtain the number of metadata corresponding to each second operational data. It can be understood that multiple operational sub-data constitute the first intersection data and the first difference data, and each operational sub-data corresponds to one metadata. The first metadata sub-column is composed of the metadata corresponding to each operational sub-data in the first intersection data. Thus, the first metadata sub-column corresponds to multiple metadata. It can be understood that the more metadata there is, the larger the data volume is, which facilitates subsequent direct allocation and calculation of the data based on the number of metadata.

[0119] All the second operation data are sorted in descending order according to the amount of the metadata to obtain the quantity sequence.

[0120] The technical solution provided by this invention sorts all the second operation data in descending order according to the amount of metadata. It can be understood that the more metadata there is, the larger the amount of data is. Therefore, it is possible to sort all the second operation data from high to low in a relatively accurate manner, which is convenient for subsequent allocation.

[0121] The server sorts each blockchain node in descending order based on the remaining CPU usage of each blockchain node, and generates a computation sequence.

[0122] The technical solution provided by this invention involves the server sorting each blockchain node in descending order based on the remaining CPU usage of each blockchain node to generate a computation sequence. It can be understood that the blockchain nodes at the beginning of the computation sequence have higher remaining computing power, which facilitates subsequent allocation based on the amount of data and the remaining computing power, resulting in higher computational efficiency.

[0123] If the number of second operation data in the quantity sequence is less than or equal to the number of blockchain nodes in the operation sequence, then the second operation data is distributed to the corresponding blockchain nodes in the operation sequence in sequence according to the quantity sequence, so that each blockchain node corresponds to at most one second operation data.

[0124] The technical solution provided by this invention is that if the number of second operation data in the quantity sequence is less than or equal to the number of blockchain nodes in the operation sequence, then the second operation data is distributed to the corresponding blockchain nodes in the operation sequence in sequence according to the quantity sequence, so that each assigned blockchain node has a corresponding second operation data. It can be understood that if the number of blockchain nodes is large, it can ensure that each second operation data can be assigned to the corresponding blockchain node, and each blockchain node...

[0125] If the number of second operation data in the quantity sequence is greater than the number of blockchain nodes in the operation sequence, then all the second operation data are distributed cyclically to the corresponding blockchain nodes in the operation sequence according to the quantity sequence, so that each blockchain node corresponds to at least one second operation data.

[0126] The technical solution provided by this invention, if the number of second operation data in the quantity sequence is greater than the number of blockchain nodes in the operation sequence, then all the second operation data are distributed cyclically to the corresponding blockchain nodes in the operation sequence according to the quantity sequence, so that each blockchain node corresponds to at least one second operation data. It can be understood that if the number of data is too large, the second operation data is first distributed to the corresponding blockchain nodes in the operation sequence according to the quantity sequence. When the last blockchain node is allocated and there is still second operation data remaining, the remaining second operation data will be distributed to the corresponding blockchain nodes in the operation sequence again in order. The cyclic distribution is to allocate the second operation data according to the remaining CPU usage of the blockchain nodes in the operation sequence from large to small. When the amount of data is too large, the remaining data is again allocated according to the remaining CPU usage of the blockchain nodes in the operation sequence from large to small, so that each blockchain node corresponds to at least one second operation data. It can be understood that the blockchain node with the largest remaining CPU usage corresponds to at least two second operation data.

[0127] This invention directly counts the amount of metadata based on a data classification table, and then sorts and distributes the second operational data. Compared with traditional technology, which requires counting the amount of data before distributing the data, this invention improves the efficiency of data distribution and thus enhances the data processing efficiency of the entire system.

[0128] The technical solution provided by this invention has two cyclic distribution schemes when distributing all the second operation data to the corresponding blockchain nodes in the operation sequence in a cyclic manner according to the quantity sequence. The first cyclic distribution scheme is to directly distribute the data again according to the order of the blockchain nodes in the operation sequence. This distribution method has the advantages of simple logic and fast distribution efficiency.

[0129] The second cyclical distribution scheme involves distributing data to blockchain nodes in the operation sequence once. At this point, each blockchain node in the operation sequence has one corresponding second operation data. The present invention then calculates the distribution coefficient of the corresponding blockchain node based on the remaining occupancy of each blockchain node and the amount of metadata of the second operation data that has been distributed. Based on the distribution coefficient of each blockchain node, all blockchain nodes are sorted in descending order to obtain a second sorted operation sequence. The second operation data that was not selected in the quantity sequence is then selected again and distributed according to the order of each blockchain node in the second sorted operation sequence.

[0130] If all blockchain nodes in the operation sequence after the second sorting redistribute the second operation data, and there is still undistributed second operation data in the quantity sequence, then the distribution coefficient of each blockchain node in the operation sequence is recalculated, and the sequence is re-sorted according to the distribution coefficient of each blockchain node. The above redistribution steps are executed until all the second operation data in the quantity sequence is distributed to multiple blockchain nodes in sequence.

[0131] The technical solution provided by this invention, when calculating the distribution coefficient, first obtains the remaining occupancy of all blockchain nodes, and then calculates the average remaining occupancy rate of multiple blockchain nodes. This invention calculates the distribution coefficient using the following formula:

[0132]

[0133] in, For the first The distribution coefficient of each blockchain node. For the first Remaining CPU usage of each blockchain node For the first Remaining CPU usage of each blockchain node This represents the upper limit of blockchain nodes. This represents the number of blockchain nodes. For space normalized values, This represents the spatial weight value. For the first The blockchain node at the ... The amount of metadata for the second computational data distributed during the second data allocation. For the first The upper limit to the number of times each blockchain node is assigned to perform a second data operation. For quantity normalization value, For quantity weight values, This is a preset constant value;

[0134] pass The average remaining occupancy rate can be obtained by... We can get the first The difference between the remaining CPU usage of each blockchain node and the average remaining CPU usage rate, when The larger the value, the greater the remaining occupancy rate of the corresponding blockchain node, and the stronger its relative computing power. Therefore, it should be allocated secondary computational data with more metadata. So if... The larger the value, the better the distribution coefficient. It tends to increase, through We can obtain the total amount of metadata distributed by a given blockchain node after multiple distributions. The larger the total amount of metadata distributed by a particular blockchain node, the greater the amount of data that the node needs to process, and the greater its processing load. Therefore, if... The larger the value, the higher the corresponding distribution coefficient. It should be as small as possible.

[0135] The technical solution provided by this invention will be based on spatial normalization values. right Normalization is performed, and then spatial weights are applied. Weighting is performed so that It is a scalar value without units. Based on the quantity normalization value... right Perform normalization processing, and then apply the quantity weight values ​​to... Weighting is performed so that It is a scalar value without units. This is to ensure the distribution coefficient... If the value is positive, the present invention will use a pre-set constant value. Among them, the preset constant value .

[0136] If the distribution coefficient The larger the value, the higher the processing efficiency of the corresponding blockchain node compared to other blockchain nodes. Therefore, the second operation data with more metadata is distributed to it first, so that all the second operation data can be distributed relatively evenly and adaptively among multiple blockchain nodes. While realizing the dynamic allocation of the second operation data, the relative load of multiple blockchain nodes is kept consistent, thereby improving the overall processing efficiency of the system.

[0137] Step S130: After performing privacy calculations on the distributed second computational data, the blockchain node obtains the third computational data and sends the third computational data to the server.

[0138] The technical solution provided by this invention involves sending the third computational data to the server after all blockchain nodes have completed privacy calculations on the distributed second computational data. This facilitates subsequent data merging by the server based on the third computational data and the data classification table.

[0139] Step S140: After the server determines that all blockchain nodes have returned the third operation data, it merges the third operation data according to the data classification table to obtain the fourth operation data and sends it to the corresponding initiator.

[0140] The technical solution provided by this invention allows the server to determine, after all blockchain nodes have fed back the third operation data, that all the second operation data have been calculated. Then, the third operation data is merged according to the data classification table to obtain the fourth operation data, which is then sent to the corresponding initiator. This invention ensures that the data calculation efficiency is improved while ensuring the accuracy of the corresponding data required by the initiator through the data classification table.

[0141] In one possible implementation of the technical solution provided by the present invention, step S140 specifically includes:

[0142] If it is determined that all blockchain nodes have fed back the third computation data, then the first dimension label and / or label group in the second cell corresponding to the ID of the initiator in the data classification table is selected.

[0143] The technical solution provided by this invention, after determining that all blockchain nodes have fed back the third operation data, selects the first dimension label and / or label group in the second cell corresponding to the ID of multiple initiating ends in the data classification table. For example, select the first dimension label corresponding to Douyin: May, June, July, August and the first dimension label corresponding to Kuaishou: July, August, September, October, so as to facilitate subsequent copying and merging based on the third operation data corresponding to the repeated first dimension label and / or label group.

[0144] Based on the first dimension label and / or label group, select the corresponding third operation data, copy and merge the selected third operation data to obtain the fourth operation data.

[0145] The technical solution provided by this invention involves the server selecting corresponding third operational data based on the first dimension tag and / or tag group. The selected third operational data is then copied and merged. For example, the first dimension tags for Douyin are May, June, July, and August, and the first dimension tags for Kuaishou are July, August, September, and October. Accordingly, the third operational data for July and August needs to be selected, copied, and merged with the third operational data for May and June to obtain the fourth operational data for Douyin. The third operational data for September and October needs to be merged with the third operational data for Kuaishou to obtain the fourth operational data. It can be understood that the fourth operational data is the calculation result.

[0146] The fourth operation data is sent to the initiating end with the corresponding ID.

[0147] The technical solution provided by this invention, after merging to obtain the fourth operation data, sends the fourth operation data to the initiating end of the corresponding ID, for example: sending the fourth operation data corresponding to May, June, July and August to Douyin.

[0148] The technical solution provided by this invention merges data through a data classification table, which ensures improved data calculation efficiency and saves computation time, while ensuring that the calculation results can be sent completely and accurately to the corresponding initiating end.

[0149] In one possible implementation, the technical solution provided by this invention further includes:

[0150] If the initiating end receives the corresponding fourth calculation data, it generates feedback information and sends the feedback information to the server.

[0151] The technical solution provided by this invention, if the initiating end receives the corresponding fourth calculation data and confirms that the data is correct, generates a feedback message and sends the feedback message to the server, so that the server can delete the calculation result according to the feedback message and prevent the leakage of the calculation result.

[0152] The server formats the fourth operation data of the corresponding ID based on the feedback information.

[0153] The technical solution provided by this invention involves the server deleting the fourth operation data of the corresponding ID that has been sent based on feedback information, thereby preventing data leakage or theft.

[0154] To better implement the privacy-based computation-based data processing method provided by this invention, this invention also provides a privacy-based computation-based data processing system, such as... Figure 3 As shown, it includes:

[0155] The classification module is used for multiple initiating ends to send task requests. The server selects the first operation data corresponding to each task request from the database according to the task request, classifies the first operation data to obtain multiple second operation data, and generates a data classification table according to the correspondence between the second operation data and the initiating ends.

[0156] The distribution module is used by the server to distribute the second computation data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation target.

[0157] The computing module is used by blockchain nodes to obtain third computing data after performing privacy calculations on the distributed second computing data, and then send the third computing data to the server.

[0158] The merging module is used by the server to merge the third operation data according to the data classification table after all blockchain nodes have returned the third operation data, to obtain the fourth operation data, and then send it to the corresponding initiator.

[0159] like Figure 4The diagram shown is a hardware structure schematic of an electronic device according to an embodiment of the present invention. The electronic device 50 includes: a processor 51, a memory 52, and a computer program; wherein...

[0160] The memory 52 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0161] The processor 51 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0162] Alternatively, the memory 52 can be either standalone or integrated with the processor 51.

[0163] When the memory 52 is a device independent of the processor 51, the device may further include:

[0164] Bus 53 is used to connect the memory 52 and the processor 51.

[0165] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0166] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0167] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0168] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method based on privacy computing, characterized in that, include: Multiple initiating ends send task requests. The server selects first computational data corresponding to each task request from the database based on the task requests, classifies the first computational data to obtain multiple second computational data, and generates a data classification table based on the correspondence between the second computational data and the initiating ends, including: Multiple initiators send task requests, and the server selects the first computation data corresponding to each task request from the database according to the task requests; The intersection of the first operation data corresponding to multiple initiators is calculated, and the intersection of the data of any two initiators is obtained as the first intersection data. Compare the first computational data of each initiating end with its corresponding first intersection data to obtain the first difference data of the corresponding initiating end; Select the first intersection data and / or first difference data corresponding to each initiating end to generate a data classification table. Each initiating end corresponds to at least one first intersection data and / or first difference data. Use the first intersection data and / or first difference data as the second operation data, including: Initialize the data classification table, and create a first column and a second column in the data classification table. The first cell in each first column has a corresponding relationship with the second cell in the second column. Enter the ID of each initiator into the first cell of the first column, extract the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and enter the first dimension label and / or label group into the corresponding second cell to generate a data classification table; The server distributes the second computational data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation objective; After performing privacy calculations on the distributed second computational data, the blockchain node obtains the third computational data and sends the third computational data to the server. After determining that all blockchain nodes have returned the third operation data, the server merges the third operation data according to the data classification table to obtain the fourth operation data and sends it to the corresponding initiator.

2. The method according to claim 1, characterized in that, The step of performing intersection calculations on the first computational data corresponding to multiple initiating ends, and determining the intersection of data from any two initiating ends to obtain the first intersection data, includes: Obtain the first dimension information of the first operation data corresponding to multiple initiating ends, decompose the first operation data according to the first dimension information to obtain at least one operation sub-data, and determine the first dimension label of each operation sub-data; Compare the first dimension labels of any two initiating sub-data, and select the sub-data with the same first dimension label as the first intersection data.

3. The method according to claim 1, characterized in that, The step of performing intersection calculations on the first computational data corresponding to multiple initiating ends, and determining the intersection of data from any two initiating ends to obtain the first intersection data, includes: Obtain first dimension information of first operation data corresponding to multiple initiating ends. If there are multiple first dimension information, decompose the first operation data according to the multiple first dimension information to obtain at least one operation sub-data. Determine the label group corresponding to each operational sub-data, wherein the label group includes first dimension information of multiple corresponding categories; Compare the label groups of any two initiating sub-data, and select the sub-data with the same label group as the first intersection data.

4. The method according to claim 1, characterized in that, The process of filling the ID of each initiator into the first cell of the first column, extracting the first dimension label and / or label group corresponding to the first intersection data and / or first difference data of the initiators, and filling the first dimension label and / or label group into the corresponding second cell to generate a data classification table includes: Obtain the metadata of all operational sub-data within the first intersection data, and generate a first metadata sub-column based on the metadata of the first intersection data; Obtain the metadata of all operational sub-data within the first difference set data, and generate a second metadata sub-column based on the metadata of the first difference set data; A third cell is created within the data classification table. Each third cell has a corresponding second cell. The first metadata sub-column and / or the second metadata sub-column are filled into the third cell corresponding to the first dimension label and / or label group.

5. The method according to claim 4, characterized in that, The server distributes the second computational data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation objective, including: Obtain the first metadata sub-column and / or the second metadata sub-column corresponding to the first intersection data and / or the first difference data for each second operation data; The number of metadata corresponding to each second operation data is obtained by summing the number of metadata in the first metadata sub-column and / or the second metadata sub-column corresponding to each second operation data. Sort all the second operation data in descending order according to the amount of the metadata to obtain a quantity sequence; The server sorts each blockchain node in descending order based on the remaining CPU usage of each blockchain node, and generates a computation sequence. If the number of second operation data in the quantity sequence is less than or equal to the number of blockchain nodes in the operation sequence, then the second operation data is distributed to the corresponding blockchain nodes in the operation sequence in sequence according to the quantity sequence, so that each blockchain node corresponds to at most one second operation data. If the number of second operation data in the quantity sequence is greater than the number of blockchain nodes in the operation sequence, then all the second operation data are distributed cyclically to the corresponding blockchain nodes in the operation sequence according to the quantity sequence, so that each blockchain node corresponds to at least one second operation data.

6. The method according to claim 5, characterized in that, After the server determines that all blockchain nodes have returned the third computation data, it merges the third computation data according to the data classification table to obtain the fourth computation data and sends it to the corresponding initiator, including: If it is determined that all blockchain nodes have fed back the third computation data, then select the first dimension label and / or label group in the second cell corresponding to the ID of the initiator in the data classification table; Based on the first dimension label and / or label group, select the corresponding third operation data, copy and merge the selected third operation data to obtain the fourth operation data; The fourth operation data is sent to the initiating end with the corresponding ID.

7. The method according to claim 6, characterized in that, Also includes: If the initiating end receives the corresponding fourth calculation data, it generates feedback information and sends the feedback information to the server. The server formats the fourth operation data of the corresponding ID based on the feedback information.

8. A privacy-based computation-based data processing system according to any one of claims 1-7, characterized in that, include: The classification module is used for multiple initiating ends to send task requests. The server selects first computational data corresponding to each task request from the database based on the task request, classifies the first computational data to obtain multiple second computational data, and generates a data classification table based on the correspondence between the second computational data and the initiating ends, including: Multiple initiators send task requests, and the server selects the first computation data corresponding to each task request from the database according to the task requests; The intersection of the first operation data corresponding to multiple initiators is calculated, and the intersection of the data of any two initiators is obtained as the first intersection data. Compare the first computational data of each initiating end with its corresponding first intersection data to obtain the first difference data of the corresponding initiating end; Select the first intersection data and / or first difference data corresponding to each initiating end to generate a data classification table. Each initiating end corresponds to at least one first intersection data and / or first difference data. Use the first intersection data and / or first difference data as the second operation data. The distribution module is used by the server to distribute the second computation data according to the remaining CPU usage of each blockchain node, so that each blockchain node has a corresponding privacy computation target. The computing module is used by blockchain nodes to obtain third computing data after performing privacy calculations on the distributed second computing data, and then send the third computing data to the server. The merging module is used by the server to merge the third operation data according to the data classification table after all blockchain nodes have returned the third operation data, to obtain the fourth operation data, and then send it to the corresponding initiator.

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