CN114580018A - Privacy computing method, device, electronic device and storage medium - Google Patents

Abstract

The present application proposes a privacy computing method, device, electronic device and storage medium, wherein the privacy computing method includes: receiving a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name; according to the target primary key list and table name, obtain the target data from the database; generate the desired data table according to the target primary key list, data filling strategy and target data; split the data in the desired data table according to the splitting strategy to obtain multiple computing nodes. Split data corresponding to each computing node; send the split data to the corresponding computing node. Thus, privacy computing can be implemented, data security can be effectively ensured, and data leakage can be avoided.

Description

Privacy computing method, device, electronic device and storage medium

technical field

The present application relates to the technical field of data processing, and in particular, to a privacy computing method, apparatus, electronic device and storage medium.

Background technique

Privacy computing refers to a collection of technologies that realize data analysis and computing on the premise of protecting the data itself from being leaked to the outside world.

Privacy computing is a computing theory and method for the protection of privacy information throughout its life cycle. system.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a privacy computing method, device, electronic device, and storage medium, which can realize privacy computing, and can effectively ensure data security and avoid data leakage.

An embodiment of the first aspect of the present application proposes a privacy computing method, including: receiving a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name; according to the target primary key list and the table name, obtain the target data from the database; generate the desired data table according to the target primary key list, data filling strategy and the target data; split the data in the desired data table according to the splitting strategy to obtain multiple Splitting data corresponding to each of the computing nodes; sending the splitting data to the corresponding computing node.

In addition, the privacy computing method according to the above-mentioned embodiment of the present application may also have the following additional technical features:

In an embodiment of the present application, the data moving and splitting instruction is obtained by: receiving a privacy calculation instruction, wherein the privacy calculation instruction includes a target primary key list and target data information; The data architecture DOA registration center obtains the distribution of target data; according to the distribution, determines a plurality of data nodes; generates the data moving and splitting instruction according to the target primary key list and the distribution, and assigns all The data moving and splitting instructions are respectively sent to the multiple data nodes.

In an embodiment of the present application, the distribution includes a table name and a table address, and the multiple data nodes include at least one primary data node and at least one secondary data node.

In an embodiment of the present application, the target data includes content data and a data primary key corresponding to the content data, and the desired data table is generated according to the target primary key list, the data filling strategy and the target data, including : generate an initial data table according to the content data and the data primary key; obtain a filling primary key from the target primary key list based on the data primary key to generate a filling primary key set; if the filling primary key set is not empty, then Obtain the filling content corresponding to each filling primary key in the filling primary key set; write the filling primary key and filling content corresponding to the filling primary key into the initial data table to generate the desired data table; if the If the filled primary key set is empty, the initial data table is used as the expected data table.

In an embodiment of the present application, splitting data in the desired data table according to a splitting strategy to obtain split data corresponding to each of the computing nodes in the plurality of computing nodes includes: determining the The number of the computing nodes in the plurality of computing nodes; according to the number and the splitting rule, the data in the desired data table is split one by one to obtain the split data corresponding to each of the computing nodes.

An embodiment of the second aspect of the present application proposes a privacy computing method, including: receiving split data, wherein the split data includes split content data and a split primary key corresponding to the split content data; creating a relocation split data table; according to the split primary key and the relocation strategy, write the split content data and/or the split primary key into the relocation split data table.

In addition, the privacy computing method according to the above-mentioned embodiment of the present application may also have the following additional technical features:

In an embodiment of the present application, writing the split content data and/or the split primary key into the relocation split data table according to the split primary key and the relocation strategy includes: If the split primary key does not exist in the relocation split data table, write the split primary key and the split content data into the relocation split data table; The split primary key, the row data corresponding to the split primary key is the filling content, and the split content data is a numerical value within the value range, then the row data is replaced with the split content data; if all The split primary key exists in the relocation split data table, the row data corresponding to the split primary key is a numerical value within the range of the value range, and the split content data is a numerical value within the range of the value range, Then, the split content data and the row data are combined and calculated according to the combining rule.

An embodiment of the third aspect of the present application proposes a privacy computing device, including: a receiving module for receiving a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name; an obtaining module, used for obtaining target data from the database according to the target primary key list and the table name; a generating module for generating a desired data table according to the target primary key list, the data filling strategy and the target data; the splitting module, for splitting the data in the desired data table according to the splitting strategy, so as to obtain split data corresponding to each of the computing nodes in the plurality of computing nodes; a sending module, configured to send the split data to the corresponding computing node.

A fourth aspect of the present application provides a privacy computing device, including: a receiving module configured to receive split data, wherein the split data includes split content data and splits corresponding to the split content data a primary key; a creation module for creating a relocation split data table; a writing module for writing the split content data and/or the split primary key into the relocation according to the split primary key and the relocation strategy Split the data table.

The embodiment of the fifth aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and running on the processor, when the processor executes the program, the above-mentioned computer program is implemented. Privacy Computing Methods.

In the electronic device of the embodiment of the present application, by executing the computer program stored in the memory by the processor, privacy calculation can be realized, and data security can be effectively ensured, and data leakage can be avoided.

Embodiments of the sixth aspect of the present application provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the privacy computing method described in the foregoing first aspect embodiment.

The computer-readable storage medium of the embodiments of the present application can implement privacy computing by storing a computer program and being executed by a processor, and can effectively ensure data security and avoid data leakage.

Additional aspects and advantages of the present application will be set forth, in part, in the following description, and in part will be apparent from the following description, or learned by practice of the present application.

Description of drawings

The above and/or additional aspects and advantages of the present application will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:

1 is a schematic flowchart of a privacy computing method according to an embodiment of the present application;

2 is a schematic flowchart of a privacy computing method according to another embodiment of the present application;

3 is a schematic diagram of a specific example of a privacy computing method according to an embodiment of the present application;

4 is a schematic flowchart of a privacy computing method according to another embodiment of the present application;

5 is a schematic flowchart of a privacy computing method according to another embodiment of the present application;

6 is a schematic flowchart of a privacy computing method according to another embodiment of the present application;

7 is a schematic structural diagram of a privacy computing method according to an embodiment of the present application;

8 is a schematic block diagram of a privacy computing device according to an embodiment of the present application;

FIG. 9 is a schematic block diagram of a privacy computing device according to another embodiment of the present application; and

FIG. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The following describes in detail the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to be used to explain the present application, but should not be construed as a limitation to the present application.

The privacy computing method, apparatus, electronic device, and storage medium of the embodiments of the present application are described below with reference to the accompanying drawings.

The privacy computing method provided in the embodiment of the present application may be executed by an electronic device, and the electronic device may be a computer or a server, etc., which is not limited herein.

In this embodiment of the present application, the electronic device may be provided with a processing component, a storage component, and a driving component. Optionally, the driving component and the processing component may be integrated and set, the storage component may store an operating system, an application program or other program modules, and the processing component implements the privacy provided by the embodiments of the present application by executing the application program stored in the storage component. calculation method.

FIG. 1 is a schematic flowchart of a privacy computing method according to an embodiment of the present application.

The privacy computing method of the embodiment of the present application can also be executed by the privacy computing device provided by the embodiment of the present application, and the device can be configured in an electronic device to receive a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key List and table name, and obtain the target data from the database according to the target primary key list and table name, and then generate the expected data table according to the target primary key list, data filling strategy and target data, and according to the split strategy. The data is split to obtain the split data corresponding to each computing node in the multiple computing nodes, and the split data is sent to the corresponding computing node, so that privacy computing can be achieved, and data security can be effectively ensured, avoiding data Give way.

As a possible situation, the privacy calculation method in the embodiment of the present application may also be executed by a data node, wherein an electronic device (for example, a server) may be the executive body of the data node, and the electronic device implements the specific details of the data node. Function.

As shown in Figure 1, the privacy computing method may include:

Step 101: Receive a data moving and splitting instruction, wherein the data moving and splitting instruction may include a target primary key list and a table name.

Specifically, after receiving the privacy calculation instruction, the above privacy computing platform can obtain the distribution of the target data from the data-oriented architecture DOA registration center according to the target data information, and then according to the distribution, determine a plurality of data nodes, and according to The target primary key list and distribution conditions generate data moving and splitting instructions, and the data moving and splitting instructions are respectively sent to multiple data nodes, which can then be received and responded to by the agent processes of the multiple data nodes.

It should be noted that the data moving and splitting instructions described in this embodiment may be generated by the privacy computing platform and sent to multiple data nodes respectively. Wherein, the privacy computing platform may be installed (set) on a server, wherein the server may include a cloud server.

In order to clearly illustrate the previous embodiment, in an embodiment of the present application, as shown in FIG. 2 , the above-mentioned data moving and splitting instructions are obtained in the following ways (steps):

Step 201: Receive a privacy calculation instruction, where the privacy calculation instruction may include a target primary key list and target data information. The target primary key list (refer to the target primary key list in FIG. 3 ) may include multiple primary keys, the target data may be data distributed in multiple data nodes, and the target data information may include basic information of the target data (for example, target data The introduction information of the data, the type information of the target data, etc.) are used to index the distribution of the target data.

It should be noted that, the privacy computing instruction described in this embodiment can be received by the privacy computing platform and relevant operations can be performed in response.

Specifically, the user can create a privacy computing instruction on a corresponding client, and send the privacy computing instruction to the privacy computing platform, and the privacy computing platform receives and responds.

As a possible situation, the privacy computing platform can detect privacy computing events in real time through related APIs (Application Programming Interface), and after detecting the privacy computing events, automatically create privacy computing instructions, and put the privacy computing instructions under the to receive and respond to the privacy computing instruction.

Step 202: Obtain the distribution of the target data from the DOA registration center of the data-oriented architecture according to the target data information. The distribution situation may include table names and table addresses, wherein the table names and table addresses may be used as indexes of data nodes, that is, the corresponding data nodes may be determined through the table names and table addresses.

In this embodiment of the present application, for data privacy protection, the original data may be distributed among multiple data nodes, wherein each data node may store part of the original data (ie, target data) in the form of a data table, to Implement distributed storage of raw data. It should be noted that the partial data (ie target data) stored by the multiple data nodes described in this embodiment may be combined into complete original data (ie target data).

Among them, the data-oriented architecture DOA registration center can register various types of data and generalized data to form a logical data resource pool, which is convenient for applications to access data. Its functions involve but are not limited to: data registration information definition, data attribute information, data classification, metadata standards, metadata classification, registration methods for different types of data, data indexing, metadata indexing, data retrieval, generalized data pattern recognition, distribution The data-oriented architecture DOA registration center stores the distribution of target data (including table name and table address).

Specifically, after receiving the above-mentioned target data information, the privacy computing platform can obtain the distribution of target data (including table name and table address) from the above-mentioned data-oriented architecture DOA registration center according to the target data information.

Step 203: Determine a plurality of data nodes according to the distribution situation. Wherein, the plurality of data nodes may include at least one primary data node and at least one secondary data node.

Specifically, after obtaining the distribution of the target data, the privacy computing platform can use the table name and table address in the distribution, or the table address as an index, to determine multiple data nodes that store the target data.

It should be noted that the plurality of data nodes described in this embodiment may further include one or more third-level data nodes, fourth-level data nodes, fifth-level data nodes . . . , which is not limited herein.

Step 204: Generate data moving and splitting instructions according to the target primary key list and distribution, and send the data moving and splitting instructions to multiple data nodes respectively. The data moving and splitting instruction is used to control the data node to perform corresponding processing on the data. Wherein, the data moving and splitting instruction may include a target primary key list and a table name.

Specifically, after determining the above-mentioned multiple data nodes, the privacy computing platform can generate a data moving and splitting instruction according to the obtained target primary key list and the distribution of the target data, and send the data moving and splitting instruction to the multiple data nodes respectively. The data nodes are received and responded by the proxy process of the plurality of data nodes.

In this embodiment of the present application, the privacy computing platform may first receive a privacy computing instruction, where the privacy computing instruction includes a target primary key list and target data information, and obtains the target data from the DOA registration center of the data-oriented architecture according to the target data information. distribution, and then determine multiple data nodes according to the distribution, generate data moving and splitting instructions according to the target primary key list and distribution, and send the data moving and splitting instructions to multiple data nodes respectively, so that privacy computing can be achieved and can be Effectively ensure data security and avoid data leakage.

Step 102: Acquire target data from the database according to the target primary key list and table name. It should be noted that the database described in this embodiment may be a database of the above-mentioned multiple data nodes, which is used to store target data.

In the embodiment of the present application, for each data node in the above-mentioned multiple data nodes, corresponding target data can be obtained from the database according to the target primary key list and table name in the received data moving and splitting instruction.

Specifically, after receiving the data moving instruction, the agent process of the data node can use the target primary key list and table name in the data moving instruction as an index to query and obtain the corresponding target data from the database of the data node.

Step 103: Generate a desired data table according to the target primary key list, the data filling strategy and the target data. Among them, the data filling strategy can be calibrated according to the actual situation.

In order to clearly illustrate the previous embodiment, in an embodiment of the present application, as shown in FIG. 4 , the target data may include content data and a data primary key corresponding to the content data. According to the target primary key list, data filling strategy and target data, the generated Desired data sheet, which can include:

In step 401, an initial data table is generated according to the content data and the data primary key.

Specifically, referring to FIG. 3 , after acquiring the above-mentioned target data, the agent process of the data node can generate a corresponding initial data table according to the content data in the target data and the data primary key corresponding to the content data.

Step 402 , obtaining the filling primary key from the target primary key list based on the data primary key to generate a filling primary key set. Wherein, the filled primary key set may include multiple filled primary keys, and the filled primary key may be a missing primary key in the initial data table relative to the target primary key set list.

In the embodiment of the present application, after the agent process of the data node obtains the above-mentioned initial data table, it can obtain the filling primary key from the target primary key list based on the data primary key of the initial data table to generate the filling primary key set.

Specifically, referring to FIG. 3, if the target primary key list includes primary keys 1, 2, 3, 4, and 5, the initial data table of the primary data node includes data primary keys 1, 2, and 4, and the initial data table of the secondary data node includes data Primary keys 2 and 3, the initial data table of the third-level data node includes data primary keys 3 and 4, then the populated primary key set of the first-level node includes populated primary keys 3 and 5, and the populated primary key set of the second-level node includes populated primary keys 1, 4, and 5 , the populated primary key set of the tertiary data node includes populated primary keys 1, 2, and 5.

Step 403 , if the filled primary key set is not empty, obtain the filled content corresponding to each filled primary key in the filled primary key set. Wherein, the filling content is a value larger than the range of the value range, wherein the range of the value range can be calibrated according to the actual situation, and no limitation is made here.

Step 404: Write the filling primary key and the filling content corresponding to the filling primary key into the initial data table to generate the desired data table.

Step 405, if the filled primary key set is empty, the initial data table is used as the expected data table.

In the embodiment of the present application, after the agent process of the data node obtains the above-mentioned filled primary key set, it can determine whether the primary key set is empty, and if not, can obtain the filling content corresponding to each filled primary key in the filled primary key set, and The filling primary key of the data node and the filling content corresponding to the filling primary key are written into the initial data table of the data node according to the size of the primary key value to generate the expected data table of the data node; if so, the data node can be The initial data sheet is used as the desired data sheet.

Specifically, assuming that the filling content corresponding to the filling primary key is the numerical value 111111 (larger than the above-mentioned range of values), referring to FIG. 3 , for the primary data node, the filling primary key 3 and the filling content “111111” corresponding to the filling primary key 3 can be used, And the filling content "111111" corresponding to the filling primary key 5 and the filling primary key 5 is written into the initial data table of the first-level data node according to the size of the primary key value to generate the expected data table of the first-level data node; for two Level data node, you can fill the primary key 1 and the filling content "111111" corresponding to the filling primary key 1, filling the primary key 4 and the filling content "111111" corresponding to the filling primary key 4, and filling the primary key 5 and the filling content corresponding to the filling primary key 5. The content "111111" is written into the initial data table of the secondary data node according to the size of the primary key value to generate the expected data table of the secondary data node; for the tertiary data node, the primary key 1 and the filling The filling content "111111" corresponding to the primary key 1, the filling content "111111" corresponding to the filling primary key 2 and the filling primary key 2, and the filling content "111111" corresponding to the filling primary key 5 and the filling primary key 5, in the order of the primary key values The initial data table of the tertiary data node is written to generate the desired data table of the tertiary data node.

Step 104: Split the data in the desired data table according to the splitting strategy to obtain split data corresponding to each of the multiple computing nodes. Among them, the splitting strategy can be calibrated according to the actual situation.

In order to clearly illustrate the previous embodiment, in an embodiment of the present application, as shown in FIG. 5 , the data in the desired data table is split according to the splitting strategy, so as to obtain the corresponding data of each computing node among the multiple computing nodes. split data, which can include:

Step 501: Determine the number of computing nodes in the plurality of computing nodes. The computing node can perform corresponding processing on the split data. It should be noted that the number of computing nodes described in this embodiment may be determined based on the foregoing splitting strategy, and the specific value thereof is not limited herein.

Step 502: Split the data in the desired data table one by one according to the quantity and splitting rules to obtain split data corresponding to each computing node. Among them, the splitting rule can be calibrated according to the actual situation.

In the embodiment of the present application, for the desired data table of each data node, the data in the desired data table can be divided into pieces of data corresponding to the number of the above computing nodes one by one, so as to obtain the split corresponding to each computing node data.

For example, referring to Figure 3, suppose there are three data computing nodes, namely, computing node 1, computing node 2, and computing node 3. For the desired data table of the first-level data node, the data primary key 1 of the desired data table can correspond to The content data 10 is split into three split data 2, 4 and 4, wherein the split data of the computing node 1 can be 2, the split data of the computing node 2 can be 4, and the split data of the computing node can be 4, and the content data 20 corresponding to the data primary key 2 of the desired data table can be split into three split data of 4, 8 and 8, wherein the split data of the computing node 1 can be 4, and the computing node 2 The split data can be 8, and the split data of the computing node can be 8; for the desired data table of the secondary data node, the content data 20 corresponding to the data primary key 2 of the desired data table can be split into 4, 8 and 8 Three split data, wherein the split data of the computing node 1 may be 4, the split data of the computing node 2 may be 8, and the split data of the computing node may be 8.

It should be noted that the data filled with content in the expected data table, such as "111111", is illegal data and will not be split.

Step 105: Send the split data to the corresponding computing node.

Specifically, after obtaining the split data of each computing node, the processing process of the data node can send the split data to the corresponding computing node, and the computing node receives and responds, thereby completing this privacy calculation.

The privacy computing method of the embodiment of the present application firstly receives a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name, and obtains target data from a database according to the target primary key list and table name, and then according to The target primary key list, data filling strategy and target data, generate the desired data table, and split the data in the desired data table according to the splitting strategy to obtain the split data corresponding to each computing node in the multiple computing nodes, and The split data is sent to the corresponding computing node, so that privacy computing can be realized, and data security can be effectively ensured to avoid data leakage.

FIG. 6 is a schematic flowchart of another privacy computing method provided according to an embodiment of the present application.

The privacy computing method in the embodiment of the present application can also be executed by the privacy computing device provided in the embodiment of the present application, and the device can be configured in an electronic device to receive split data, where the split data includes split content data and split data. Split the primary key corresponding to the content data, then create a relocation and split data table, and write the split content data and/or the split primary key into the relocation and split data table according to the split primary key and the relocation strategy, so that privacy computing can be achieved , and can effectively ensure data security and avoid data leakage.

As a possible situation, the privacy computing method in this embodiment of the present application may also be executed by a computing node, where an electronic device (for example, a server) may be the execution body of the computing node, and the electronic device implements the specific details of the computing node. Function.

As shown in Figure 6, the privacy computing method may include:

Step 601: Receive split data, where the split data may include split content data and a split primary key corresponding to the split content data.

Specifically, after receiving the data moving and splitting instruction sent by the above-mentioned privacy computing platform, the agent process of the data node can obtain the target data from the database according to the target primary key list and table name in the data splitting instruction, and according to The target primary key list, data filling strategy and target data, generate the desired data table, and then split the data in the desired data table according to the splitting strategy to obtain the split data corresponding to each computing node in the multiple computing nodes, and The split data is sent to the corresponding computing node, and the split data is received by the computing node.

Step 602, creating a relocation split data table.

Specifically, after the computing node (ie, the proxy process of the computing node) receives the above-mentioned split data, it can create a relocation and split data table based on a preset creation rule. At this time, the relocation and split data table has not yet been written into the split data table. The split primary key corresponding to the content data and the split content data is an empty table.

It should be noted that the preset creation rules described in this embodiment may be calibrated according to actual conditions.

Step 603, according to the split primary key and the relocation strategy, write the split content data and/or the split primary key into the relocation split data table. Among them, the relocation strategy can be calibrated according to the actual situation.

In order to clearly illustrate the previous embodiment, in an embodiment of the present application, according to the split primary key and the relocation strategy, writing the split content data and/or the split primary key into the relocation split data table may include: If there is no split primary key in the split data table, the split primary key and split content data will be written into the relocation split data table; if there is a split primary key in the relocated split data table, the row data corresponding to the split primary key will be filled. , and the split content data is a value within the value range, then replace the row data with the split content data; if there is a split primary key in the relocated split data table, the row data corresponding to the split primary key is within the range of the value range. If the split content data is a numerical value within the value range, the split content data and row data will be merged and calculated according to the merging rules. Among them, the merging rules can be calibrated according to the actual situation.

In this embodiment of the present application, after receiving the split data and creating the relocation split data table, the computing node can split content data and/or split data in the split data according to the split primary key and the relocation strategy. The split primary key is written to the relocated split data table. Among them, the preset writing rule can be calibrated according to the actual situation.

Specifically, when the computing node writes the above-mentioned split content data and/or the split primary key into the above-mentioned relocation and split data table, it needs to first make the following judgment on the relocation and split data table:

If there is no split primary key in the relocation split data table, it means that no split data (ie, the split primary key and its corresponding split content data) has been written in the relocation split data table, that is, the split data table is relocated If there is no data in the row of the split primary key and its corresponding split data, the above split primary key and split content data can be written into the relocation split data table.

If there is a split primary key in the relocation split data table, it means that the split primary key and split content data have been written into the relocation split data table. At this time, if the row data corresponding to the split primary key that has been written is filled content (that is, illegal data, such as "111111"), and the current split content data to be written is a value within the value range, the row data can be replaced with the current split content data to be written.

If there is a split primary key in the relocation split data table, it means that the split primary key and split content data have been written to the relocation split data table. At this time, if the row data corresponding to the split primary key that has been written is a value If the split content data currently needs to be written is the value within the value range, the current split content data to be written and the row data can be combined and calculated according to the merge rule, such as adding.

If there is a split primary key in the relocation split data table, it means that the split primary key and split content data have been written into the relocation split data table. That is, illegal data, such as "111111"), you can do nothing.

In this way, multiple distributed data tables can be combined into multiple data tables that satisfy privacy calculations (for example, three data tables stored in computing node 1, computing node 2, and computing node 3 respectively), according to the privacy calculation It completes the privacy calculation and also protects the data information of each data node from being leaked.

The privacy computing method of the embodiment of the present application firstly receives split data, wherein the split data includes split content data and a split primary key corresponding to the split content data, and then creates a relocation split data table, and divides the data according to the split primary key and the split primary key. The relocation strategy is to write the split content data and/or the split primary key into the relocation split data table, so that privacy calculations can be realized, data security can be effectively ensured, and data leakage can be avoided.

In order for those skilled in the art to more clearly understand the privacy calculation method provided by the embodiments of the present application, FIG. 7 is a schematic diagram of the architecture of the privacy calculation method. As shown in FIG. 7 , multiple data nodes may be determined based on the DOA registration center. The data nodes may include tier 1 data nodes 1, tier 2 data nodes 2-1 and 2-2, and tier 3 data nodes 3-1 and 3-2, the multiple data nodes may send split data to multiple computing nodes , the multiple computing nodes may include a computing node 1, a computing node 2, and a computing node 3, and the multiple computing nodes write the split data into the relocation split data table. Thus, privacy computing can be implemented, data security can be effectively ensured, and data leakage can be avoided.

FIG. 8 is a schematic structural diagram of a privacy computing device according to an embodiment of the present application.

The privacy computing device of the embodiment of the present application can be configured in an electronic device to receive a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name, and according to the target primary key list and table name, from Obtain the target data from the database, and then generate the desired data table according to the target primary key list, data filling strategy and target data, and split the data in the desired data table according to the splitting strategy to obtain each calculation in multiple computing nodes. The split data corresponding to the node, and the split data is sent to the corresponding computing node, so that privacy computing can be achieved, and data security can be effectively ensured to avoid data leakage.

As shown in FIG. 8 , the privacy computing device 800 may include: a receiving module 810 , an obtaining module 820 , a generating module 830 , a splitting module 840 and a sending module 850 .

The receiving module 810 is configured to receive a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name.

The obtaining module 820 is configured to obtain target data from the database according to the target primary key list and the table name.

The generating module 830 is configured to generate a desired data table according to the target primary key list, the data filling strategy and the target data.

The splitting module 840 is configured to split the data in the desired data table according to the splitting strategy, so as to obtain split data corresponding to each computing node in the multiple computing nodes.

The sending module 850 is configured to send the split data to the corresponding computing node.

In an embodiment of the present application, the target data includes content data and a data primary key corresponding to the content data, and the generating module 830 is specifically configured to: generate an initial data table according to the content data and the data primary key; Get the populated primary key to generate the populated primary key set; if the populated primary key set is not empty, obtain the populated content corresponding to each populated primary key in the populated primary key set; write the populated primary key and the populated content corresponding to the populated primary key into the initial data table to generate Expected data table; if the populated primary key set is empty, the initial data table is used as the expected data table.

In an embodiment of the present application, the splitting module 840 is specifically configured to: determine the number of computing nodes in the plurality of computing nodes; split the data in the desired data table one by one according to the number and splitting rules to obtain each Split data corresponding to each computing node.

It should be noted that, the foregoing explanations of the embodiment of the privacy computing method in FIG. 1 to FIG. 5 are also applicable to the privacy computing device of this embodiment, and details are not repeated here.

In the privacy computing device of the embodiment of the present application, firstly, the receiving module receives a data moving and splitting instruction, wherein the data moving and splitting instruction includes a target primary key list and a table name, and the acquisition module retrieves the data from the database according to the target primary key list and the table name. Obtain the target data, and then generate the desired data table according to the target primary key list, data filling strategy and target data through the generation module, and split the data in the desired data table through the splitting module according to the splitting strategy to obtain multiple calculations The split data corresponding to each computing node in the node, and the split data is sent to the corresponding computing node through the sending module, so that privacy computing can be realized, data security can be effectively ensured, and data leakage can be avoided.

FIG. 9 is a schematic structural diagram of another privacy computing device provided according to an embodiment of the present application.

The privacy computing device of this embodiment of the present application can be configured in an electronic device to receive split data, where the split data includes split content data and a split primary key corresponding to the split content data, and then creates, relocates, and split data Table, and according to the split primary key and the relocation strategy, the split content data and/or the split primary key are written into the relocation split data table, so that privacy calculations can be achieved, and data security can be effectively ensured to avoid data leakage.

As shown in FIG. 9 , the privacy computing device 900 may include: a receiving module 910 , a creating module 920 and a writing module 930 .

The receiving module 910 is configured to receive split data, wherein the split data includes split content data and a split primary key corresponding to the split content data.

The creation module 920 is used to create the relocation split data table.

The writing module 930 is configured to write the split content data and/or the split primary key into the relocation split data table according to the split primary key and the relocation strategy.

In an embodiment of the present application, the writing module 930 is specifically configured to: if the split primary key does not exist in the relocation split data table, write the split primary key and split content data into the relocation split data table; If there is a split primary key in the relocated split data table, the row data corresponding to the split primary key is the filling content, and the split content data is a value within the value range, then replace the row data with the split content data; if the split is relocated and split If there is a split primary key in the data table, the row data corresponding to the split primary key is a value within the value range, and the split content data is a value within the value range, then the split content data and row data are merged according to the merging rules .

It should be noted that, the foregoing explanations of the embodiment of the privacy computing method in FIG. 3 and FIG. 6 to FIG. 7 are also applicable to the privacy computing device of this embodiment, and details are not repeated here.

The privacy computing device of the embodiment of the present application firstly receives split data through the receiving module, wherein the split data includes split content data and split primary keys corresponding to the split content data, and then creates a relocation split data table through the creation module, And through the writing module, according to the split primary key and the relocation strategy, the split content data and/or the split primary key are written into the relocation split data table, so that privacy computing can be achieved, and data security can be effectively ensured to avoid data leakage.

In order to realize the above embodiments, as shown in FIG. 10 , the present application further proposes an electronic device 1000 , which includes a memory 1010 , a processor 1020 , and a computer program stored in the memory 1010 and running on the processor 1020 , the processor 1020 The program is executed to implement the privacy computing method proposed by the foregoing embodiments of the present application.

In the electronic device of the embodiment of the present application, by executing the computer program stored in the memory by the processor, privacy calculation can be implemented, and data security can be effectively ensured, and data leakage can be avoided.

In order to implement the above-mentioned embodiments, the present application further provides a non-transitory computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the privacy computing method proposed in the foregoing embodiments of the present application.

The computer-readable storage medium of the embodiments of the present application, by storing a computer program and being executed by a processor, can implement privacy computing, and can effectively ensure data security and avoid data leakage.

In the description of this specification, the terms "first" and "second" are only used for the purpose of description, and cannot be understood as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present application, "plurality" means at least two, such as two, three, etc., unless expressly and specifically defined otherwise.

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present application. Embodiments are subject to variations, modifications, substitutions and variations.

Claims (11)

1. A privacy computing method, comprising:

receiving a data moving and splitting instruction, wherein the data moving and splitting instruction comprises a target main key list and a list name;

acquiring target data from a database according to the target primary key list and the list name;

generating an expected data table according to the target primary key list, the data filling strategy and the target data;

splitting data in the expected data table according to a splitting strategy to obtain split data corresponding to each computing node in a plurality of computing nodes;

and sending the split data to corresponding computing nodes.

2. The privacy computing method of claim 1, wherein the data migration split instruction is obtained by:

receiving a privacy calculation instruction, wherein the privacy calculation instruction comprises a target primary key list and target data information;

acquiring the distribution condition of target data from a data-oriented architecture DOA (data oriented architecture) registration center according to the target data information;

determining a plurality of data nodes according to the distribution condition;

and generating the data moving and splitting instruction according to the target primary key list and the distribution condition, and respectively sending the data moving and splitting instruction to the plurality of data nodes.

3. The privacy computing method of claim 2, wherein the distribution comprises a table name and a table address, and wherein the plurality of data nodes comprises at least one primary data node and at least one secondary data node.

4. The privacy computation method of claim 1, wherein the target data comprises content data and a data primary key corresponding to the content data, and the generating an expected data table according to the target primary key list, a data population policy and the target data comprises:

generating an initial data table according to the content data and the data primary key;

acquiring a filling main key from the target main key list based on the data main key to generate a filling main key set;

if the filling main key set is not empty, acquiring filling content corresponding to each filling main key in the filling main key set;

writing the filling main key and filling contents corresponding to the filling main key into the initial data table to generate the expected data table;

and if the filling main key set is empty, taking the initial data table as the expected data table.

5. The privacy computation method of claim 1, wherein splitting data in the expected data table according to a splitting policy to obtain split data corresponding to each of a plurality of computing nodes comprises:

determining a number of the compute nodes in the plurality of compute nodes;

and splitting the data in the expected data table one by one according to the number and the splitting rule to obtain split data corresponding to each computing node.

6. A privacy computing method, comprising:

receiving splitting data, wherein the splitting data comprises splitting content data and a splitting primary key corresponding to the splitting content data;

creating a relocation splitting data table;

and writing the splitting content data and/or the splitting primary key into the moving splitting data table according to the splitting primary key and the moving strategy.

7. The privacy computation method of claim 6, wherein the writing the split content data and/or the split primary key into the migration split data table according to the split primary key and a migration policy comprises:

if the main splitting key does not exist in the moved splitting data table, writing the main splitting key and the splitting content data into the moved splitting data table;

if the splitting primary key exists in the moved splitting data table, the line data corresponding to the splitting primary key is filling content, and the splitting content data is a numerical value in a value range, replacing the line data with the splitting content data;

and if the moving splitting data table has the splitting primary key, the line data corresponding to the splitting primary key is a numerical value in the value range, and the splitting content data is a numerical value in the value range, merging and calculating the splitting content data and the line data according to a merging rule.

8. A privacy computing device, comprising:

the system comprises a receiving module, a processing module and a display module, wherein the receiving module is used for receiving a data moving and splitting instruction, and the data moving and splitting instruction comprises a target main key list and a list name;

the acquisition module is used for acquiring target data from a database according to the target primary key list and the list name;

the generating module is used for generating an expected data table according to the target main key list, the data filling strategy and the target data;

the splitting module is used for splitting the data in the expected data table according to a splitting strategy so as to obtain split data corresponding to each computing node in a plurality of computing nodes;

and the sending module is used for sending the split data to the corresponding computing node.

9. A privacy computing device, comprising:

the system comprises a receiving module, a judging module and a sending module, wherein the receiving module is used for receiving splitting data, and the splitting data comprises splitting content data and a splitting primary key corresponding to the splitting content data;

the creating module is used for creating a relocation and splitting data table;

and the writing module is used for writing the splitting content data and/or the splitting main key into the moving splitting data table according to the splitting main key and the moving strategy.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the privacy computation method of any one of claims 1-5 or claims 6-7 when executing the program.

11. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the privacy calculation method of any one of claims 1-5 or claims 6-7.

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