WO2024016547A1

WO2024016547A1 - Data query method and device based on multi-party collaboration

Info

Publication number: WO2024016547A1
Application number: PCT/CN2022/135099
Authority: WO
Inventors: 马超; 杨仁慧; 孙善禄; 黄胜
Original assignee: 蚂蚁区块链科技(上海)有限公司
Priority date: 2022-07-22
Filing date: 2022-11-29
Publication date: 2024-01-25
Also published as: CN115269683A

Abstract

One or more embodiments of the present description provide a data query method and device based on multi-party collaboration. The method comprises: acquiring a data query request inputted by a user for a virtual data set; parsing the data query request to obtain at least one sub-operation related to the data query request; selecting at least one instantiation operator corresponding to the at least one sub-operation from an instantiation operator library related to privacy computing, and on the basis of the at least one instantiation operator, generating a query flow corresponding to the data query request; and creating a smart contract comprising query logic corresponding to the query flow, and deploying the created smart contract to a blockchain network docking a data collaboration platform for the user to call.

Description

A data query method and device based on multi-party collaboration

Technical field

Multiple embodiments of this specification relate to the field of blockchain technology, and in particular, to a data query method and device based on multi-party collaboration.

Background technique

Privacy computing refers to the technology that realizes data analysis and calculation on the premise of protecting the data itself from being leaked to the outside world. For example, the current mainstream privacy computing technologies usually include FL (Federated Learning) technology, MPC (Secure Multi-party Computation, multi-party secure computing) technology, TEE (Trusted Execute Environment, Trusted Execution Environment) technology, etc. wait. In practical applications, the data involved in privacy calculations may come from multiple different data sources. Therefore, how to use data from multiple different data sources to perform privacy calculations has been a focus of the industry.

Contents of the invention

This specification proposes a data query method based on multi-party collaboration. The blockchain-based data collaboration platform connects multiple collaboration participants; the method is applied to any target collaboration participant among the multiple collaboration participants. Corresponding server; the method includes: obtaining a data query request for a virtual data set input by the user; wherein the virtual data set is maintained by the data collaboration platform for the multiple collaboration participants for participation. The data of privacy calculation is a virtual data set obtained by performing data virtualization processing; parsing the data query request and obtaining at least one sub-operation related to the data query request; from the instantiation operator library related to the privacy calculation Select at least one instantiation operator corresponding to the at least one sub-operation, and generate a query process corresponding to the data query request based on the at least one instantiation operator; create a query including a query corresponding to the query process logical smart contract, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.

This specification also proposes a data query method based on multi-party collaboration, which method is applied to a data collaboration platform based on blockchain; wherein the data collaboration platform connects multiple collaboration participants; the method includes: obtaining all Any target collaboration participant among the plurality of collaboration participants uploads a data query request for a virtual data set input by the user from the corresponding server; wherein the virtual data set is the data collaboration platform's query request for the multiple collaboration participants. The data maintained by each collaborative participant for participating in privacy calculations is a virtual data set obtained through data virtualization processing; parsing the data query request, obtaining at least one sub-operation related to the data query request; and obtaining at least one sub-operation related to the data query request; Select at least one instantiation operator corresponding to the at least one sub-operation from the privacy computing-related instantiation operator library, and generate a query process corresponding to the data query request based on the at least one instantiation operator; Create a smart contract containing query logic corresponding to the query process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.

This specification also proposes a data query device based on multi-party collaboration. The blockchain-based data collaboration platform connects multiple collaboration participants; the method is applied to collaborate with any target among the multiple collaboration participants. The server corresponding to the party; the device includes: a first acquisition module to obtain the data query request for the virtual data set input by the user; wherein the virtual data set is the data collaboration platform for the multiple collaboration participants The data maintained separately for participating in privacy calculations is a virtual data set obtained by performing data virtualization processing; the first parsing module parses the data query request and obtains at least one sub-operation related to the data query request; the first A generation module that selects at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy calculation, and generates the data query based on the at least one instantiation operator. Request the corresponding query process; the first deployment module creates a smart contract containing query logic corresponding to the query process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform, for the user to call.

This specification also proposes a data query device based on multi-party collaboration, which device is applied to a data collaboration platform based on blockchain; wherein the data collaboration platform connects multiple collaboration participants; the device includes: a second The acquisition module acquires the data query request for the virtual data set input by the user and uploaded by the corresponding server of any target collaboration participant among the plurality of collaboration participants; wherein the virtual data set is the The data collaboration platform performs data virtualization processing on the data maintained by the multiple collaboration participants for participating in privacy calculations and obtains a virtual data set; the second parsing module parses the data query request to obtain the data related to the data. At least one sub-operation related to the query request; the second generation module selects at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy calculation, and generates the An instantiation operator generates a query process corresponding to the data query request; a second deployment module creates a smart contract containing query logic corresponding to the query process, and deploys the created smart contract to the The data collaboration platform is connected to the blockchain network for the users to call.

The technical solutions described above in this manual not only reduce the complexity of data usage when performing privacy calculations on data from multiple data sources, but also make the data usage process more transparent, credible and easy to trace. Moreover, because users can customize data query needs from a global data perspective based on virtual data collections obtained by virtual machine processing of data from multiple data sources, and based on the privacy computing capabilities of the data collaboration platform itself, Different data privacy query processes are planned according to the different data query needs of users, so that different data privacy query scenarios can be flexibly adapted, so that the data collaboration platform can activate more data usage scenarios.

Description of drawings

Figure 1 is a system architecture diagram of a data collaboration platform according to an exemplary embodiment of this specification.

Figure 2 is a flow chart of a data query method based on multi-party collaboration according to an exemplary embodiment of this specification.

FIG. 3 is a schematic diagram illustrating virtualization processing of data maintained by multiple collaboration participants according to an exemplary embodiment of this specification.

Figure 4 is a flowchart of another data query method based on multi-party collaboration illustrated in this specification according to an exemplary embodiment.

FIG. 5 is a schematic structural diagram of an electronic device provided by an exemplary embodiment.

Figure 6 is a block diagram of a data query device based on multi-party collaboration according to an exemplary embodiment of this specification.

FIG. 7 is a block diagram of another data query device based on multi-party collaboration according to an exemplary embodiment of this specification.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described The embodiments are only some of the embodiments of this specification, but not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of this specification.

It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, methods may include more or fewer steps than described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments; and multiple steps described in this specification may also be combined into a single step in other embodiments. describe.

In scenarios where privacy computing is performed on data from multiple different data sources, in order to break the information silos between various data sources and achieve sufficient data integration, a centralized data collaboration platform is usually built. This centralized data collaboration platform is usually responsible for scheduling and managing data from various data sources. When privacy calculations need to be performed on data from multiple different data sources, the data collaboration platform can query data from different data sources separately, and perform privacy calculations on the queried data based on its own privacy computing capabilities.

However, in actual applications, different data sources usually have different security requirements for the use of the data they maintain; for example, some data sources may prohibit the export of data where they are located for the purpose of privacy protection. domain, or does not allow the data it maintains to be aggregated with data maintained by other data sources, etc.

The differences in data security requirements of different data sources may lead to a large number of data query and privacy calculation scenarios. Therefore, it is of great significance for a data collaboration platform to flexibly adapt to these data query and privacy computing scenarios.

Based on this, this specification proposes a scenario where privacy computing is performed based on data maintained by multiple data sources. Users can customize query data requirements from a global data perspective and flexibly implement privacy computing based on the data collaboration platform's own privacy computing capabilities. The technical solution of the data query process corresponding to the planning and user-customized data query requirements.

During implementation, a data collaboration platform based on blockchain can be built, which can connect multiple collaboration participants. Each collaboration participant can maintain data used to participate in privacy calculations. The data collaboration platform can perform data virtualization processing on the data maintained by each collaboration participant based on data virtualization technology to obtain a virtual data collection.

For any target collaboration participant among the multiple collaboration participants, a user who accesses the server corresponding to the target collaboration participant can input a data query request for the virtual data set. After obtaining the data query request, the server corresponding to the target protocol participant can parse the data query request and obtain at least one sub-operation related to the data query request; and then obtain at least one sub-operation related to the data query request from the data collaboration platform management and privacy From the calculation-related instantiation operator library, select at least one instantiation operator corresponding to at least one of the above sub-operations, and generate a query process corresponding to the above data query request based on the above at least one instantiation operator.

Finally, a smart contract containing query logic corresponding to the query process can be created, and the created smart contract can be deployed to the blockchain network connected to the data collaboration platform. Subsequently, users can complete the query process corresponding to the above data query request by calling the smart contract.

In the above technical solution, it can not only reduce the complexity of data usage when performing privacy calculations on data from multiple data sources, but also make the data usage process more transparent, credible and easy to trace.

Moreover, because users can customize data query needs from a global data perspective based on virtual data collections obtained by virtual machine processing of data from multiple data sources, and based on the privacy computing capabilities of the data collaboration platform itself, Different data privacy query processes are planned according to the different data query needs of users, so that different data privacy query scenarios can be flexibly adapted, so that the data collaboration platform can activate more data usage scenarios.

Please refer to Figure 1, which is a system architecture diagram of a data collaboration platform according to an exemplary embodiment of this specification.

As shown in Figure 1, the above-mentioned data collaboration platform can be a blockchain-based privacy collaboration platform, which can connect to a blockchain network and multiple collaboration participants (that is, multiple data sources). The data collaboration platform can perform calculation and scheduling on the data maintained by multiple collaboration participants, and complete privacy calculations from a global data perspective.

For example, on the premise that the data security requirements of each collaboration participant are met, the data maintained by each collaboration participant is used for privacy calculations as a whole.

For each collaboration participant, data sets and calculation result sets can be maintained separately. Among them, the data set is used to store data participating in privacy calculations. The above calculation result set is used to store the calculation results obtained by performing privacy calculations on the data in the above data set. The calculation result may specifically be an intermediate calculation result or a final calculation result.

It should be noted that the above data set and the above calculation result set can be a database. For different collaboration participants, the database used may be a homogeneous database or a heterogeneous database, which is not particularly limited in this specification.

In addition to the above-mentioned data sets and calculation result sets, each collaboration participant can also be equipped with several instantiation operators and functional components related to privacy computing.

As shown in Figure 1, the instantiation operators related to privacy computing carried by each collaboration participant can specifically include MPC-based instantiation operators, TEE-based instantiation operators, and TL-based instantiation operators, etc. wait. Each instantiation operator corresponds to a computing operation or computing function based on a specific privacy computing technology. For a specific computing operation or computing function, it can correspond to a variety of instantiation operators based on a variety of privacy computing technologies.

For example, taking the calculation function to be implemented as intersection calculation as an example, the instantiation operator corresponding to the intersection calculation function may specifically include an instantiation operator for intersection calculation based on MPC, and an intersection calculation based on TEE. instantiation operator, etc. That is to say, for the intersection calculation function, two instantiation operators can be implemented based on MPC technology and TEE technology respectively.

As shown in Figure 1, the functional components carried by each collaboration participant can include, for example, the following:

The data query parsing component is used to parse the data query request input by the user, obtain the data query requirements expressed by the data query request, and parse the data query request into at least one sub-operation related to the data query. For example, in practical applications, the above data query request can be a data query statement (such as a SQL statement), and the data query parsing component is used to perform syntax analysis on the data query statement to obtain the syntax tree of the data query statement, and then The user's query requirements are clarified based on the syntax structure described by the obtained syntax tree, and the user's query requirements are converted into at least one sub-operation related to the data query.

The data query planning component is used to plan a basic query process corresponding to the above data query request based on the at least one sub-operation parsed by the above data query parsing component.

The application execution planning component is used to select at least one instantiation operator corresponding to at least one of the above sub-operations from the instantiation operator library managed by the data collaboration platform, and then plan an instantiation operator corresponding to the above basic operation based on the at least one instantiation operator. The instantiated query process corresponding to the query process. At this time, the instantiated query process is a specific data collaboration application for querying data planned based on the data query requirements expressed by the data query request input by the user.

The application contract management component is used to generate execution code related to the query logic corresponding to the above instantiation query process, compile the generated execution code into smart contract code, and deploy it in the blockchain network connected to the data collaboration platform. Smart contract with the above smart contract code.

The execution scheduling component is used to interact with the above-mentioned smart contracts and further schedule local instantiation operators to complete related privacy calculations by monitoring the call results of the smart contracts stored on the blockchain.

It should be noted that the functional components shown above are only illustrative. In actual applications, some of the components can be integrated based on actual needs, or some of the components can be further split. , is not particularly limited in this specification.

It should also be noted that, among the functional components shown above, other components except the execution scheduling component can be deployed in a distributed manner on each collaboration participant by the data collaboration platform (Figure 1 shows this distributed deployment In the case of each collaboration participant), it can also be deployed centrally on the data collaboration platform, which is not specifically limited in this specification. For example, it can be deployed centrally on a blockchain service platform (not shown in Figure 1) used to manage the above-mentioned blockchain network.

Please continue to refer to Figure 1. Each collaboration participant can also provide an application execution client for users.

The application executes the client and is specifically used to provide users with access services to the data collaboration platform. Users can execute the client through the application, initiate contract calls for the above smart contracts, and execute the client through the application to query the smart contract. The call result of the contract.

Please refer to Figure 2. Figure 2 is a flow chart of a data query method based on multi-party collaboration illustrated in this specification according to an exemplary embodiment. This method can be applied to any of the multiple collaboration participants shown in Figure 1 The server corresponding to the target collaboration participant; the method includes steps 202 to 208.

Step 202: Obtain a data query request for a virtual data set input by the user; wherein the virtual data set is maintained by the data collaboration platform for at least some of the multiple collaboration participants for participation. Privacy computing data is a virtual data collection obtained through data virtualization processing.

The above-mentioned collaboration participants can specifically access data providers of the data collaboration platform. For example, in one example, the above-mentioned multiple collaboration participants may specifically include multiple data centers distributed in different regions.

Each collaboration participant can maintain a data collection locally for participating in privacy calculations. In order to break the information silos between various collaboration participants, the data collaboration platform can use data virtualization technology to perform data virtualization processing on the data sets maintained by each collaboration participant.

Data virtual machine technology is a kind of mapping of physical data maintained by multiple data sources into virtual data at the logical level, and then integrating the physical data maintained by multiple data sources into a logical virtual data collection (also known as is a logical view), a technology used by upper-layer applications. By performing data virtualization on the data sets maintained by each collaboration participant, the data sets maintained by each collaboration participant can be integrated into a virtual data collection for users to use.

Among them, when mapping physical data maintained by multiple data sources into virtual data, it usually refers to the process of mapping physical data attributes contained in the physical data into virtual data attributes. It should be noted that when mapping the physical data attributes contained in the physical data to virtual data attributes, a one-to-one mapping method can be used to map a physical data attribute contained in the physical data to a corresponding virtual data attribute. , you can also use many-to-one mapping to map multiple physical data attributes contained in physical data into a corresponding virtual data attribute.

For example, please refer to FIG. 3 , which is a schematic diagram of performing data virtualization processing on data maintained by multiple collaborative participants according to an exemplary embodiment of this specification.

As shown in Figure 3, assume that the data stored in data set 1 maintained by collaboration participant 1 includes attributes 1-3, and the data stored in data set 2 maintained by collaboration participant 2 includes attributes 4-7, and that maintained by collaboration participant 3 The data stored in dataset 3 includes attributes 8-11. Among them, each attribute can represent a field in the data table. The virtual data table obtained after performing data virtualization processing on the data stored in data sets 1-3 can contain attributes A-F.

Among them, the data virtualization processing method shown in Figure 3 shows that attribute 1 in data set 1, attribute 4 in data set 2, and attribute 8 in data set 3 adopt a many-to-one mapping method. Became attribute A in the virtual data table; attribute 2 in data set 1, attribute 5 in data set 2, and attribute 9 in data set 3 were mapped into the virtual data table using many-to-one mapping. Attribute B; attribute 3 in data set 1 and attribute 6 in data set 2 are mapped to attribute C in the virtual data table using many-to-one mapping. Attribute 10 in data set 3 is mapped to attribute D in the virtual data table using one-to-one mapping; attribute 7 in data set 2 is mapped to a virtual data table using one-to-one mapping. Attribute E in Data Set 3; Attribute 11 in Data Set 3 is mapped to attribute F in the virtual data table using one-to-one mapping.

It should be emphasized that the data mapping method shown in Figure 3 is only exemplary. In actual applications, the data mapping method used when performing data virtualization on data maintained by multiple collaborative participants usually depends on the data. The data semantics itself are not particularly limited in this specification; for example, when performing data mapping, if the data semantics of multiple data fields distributed in different collaboration participants are related, then the multiple data fields can be mapped Become a virtual data attribute.

After the data virtualization processing is completed for the data sets maintained by each collaboration participant, the obtained virtual data table can be distributed to each collaboration participant, and each collaboration participant will further use the virtual data table as a global reflection of each collaboration participant. The data view of the data status maintained by the collaboration participants is output and displayed to the user.

When users who access the above-mentioned target collaboration participants through a client have data query needs, they can input a data query request for the virtual data table through the client, and initiate a data query from a global data perspective.

Among them, the above-mentioned data query request is specifically used to express the user's data query needs to the data collaboration platform. The specific form of the data query request is not particularly limited in this specification. For example, in one example, the above data query request may be a data query statement (such as a SQL statement) input by the user.

Step 204: Parse the data query request and obtain at least one sub-operation related to the data query request.

In practical applications, the user's data query operation for the above virtual data collection is usually a complete query process composed of at least one sub-operation.

For example, assume that the above virtual data collection is a user information table that stores user personal information. The information table includes gender fields and age fields. If the user's data query requirement for the user information table is to "query the average age data of male users stored in the user information table", then the user's data query operation for the above virtual data collection at this time is performed by "for Query sub-operation for the user information of male users in the user information table", "Filter calculation sub-operation for the age data in the user information of male users in the user information table", "Average calculation for the filtered age data A query process composed of "value calculation sub-operations".

After the server corresponding to the above-mentioned target collaboration participant obtains the data query request input by the user through the client, it can parse the data query request to clarify the user's data query requirements for the above-mentioned virtual data collection, and then combine the above-mentioned The data query request is parsed into at least one sub-operation related to the data query.

For example, please continue to refer to Figure 1. After the server corresponding to the above-mentioned target collaboration participant obtains the data query request input by the user through the client, it can call the data query parsing component described in Figure 1 to parse the data query request. .

Among them, the specific process of parsing the above data query request usually depends on the specific form of the data query request.

For example, in the illustrated implementation, the above-mentioned data query request may specifically be a data query statement. For data query statements, users' data query needs are usually organized and expressed through certain grammatical structures. In this case, syntax analysis can be performed on the data query statement to obtain a syntax tree corresponding to the data query statement. Among them, syntax trees are usually used to describe the syntax structure of data query statements. After the syntax tree is obtained through syntax parsing, the user's data query requirements can be clarified based on the syntax structure described in the syntax tree, and at least one sub-operation related to the data query statement can be determined.

Among them, it should be noted that the specific process of syntactic parsing of the data query statement will not be described in detail in this specification. When implementing the technical solutions recorded in this specification, those skilled in the art can refer to the related technologies. records. For example, some general grammar parsing engines can be used to perform grammar parsing on the above data query statements.

In an embodiment shown, the above-mentioned data query request may specifically be a request message for data query. For request messages used for data query, the user's data query needs are usually organized and expressed through a certain message format. In this case, a message format analysis can be performed on the request message to clarify the user's data query requirements and determine at least one sub-operation related to the request message.

Among them, it should be noted that the specific process of parsing the message format of the request message will not be described in detail in this specification. Those skilled in the art can refer to the relevant information when implementing the technical solutions recorded in this specification. Recorded in technology. For example, some common message parsing tools or protocols can be used to parse the message format of the above request message.

In this specification, the above-mentioned data query request may be a request only for performing data query on the virtual data in the above-mentioned virtual data set, or may be a request for performing data query and calculation on the virtual data in the above-mentioned virtual data set. request.

In an embodiment shown, if the above data query request is a request only for performing data query on the virtual data in the above virtual data collection, in this case, the above at least one sub-operation usually only includes At least one query sub-operation for the virtual data in the above virtual data collection.

For example, assuming that the above virtual data set is a user information table that stores users' personal information, such a data query request can usually be a data query request such as "query the male users stored in this user information table." In this case, the above-mentioned at least one sub-operation usually only includes at least one "query sub-operation for user information of male users in the user information table".

It should be noted that since the virtual data in the virtual data collection contains several virtual data fields that are obtained by mapping and integrating the physical data fields contained in the physical data maintained by each collaborative participant, the above-mentioned several virtual data The data content corresponding to the fields is actually maintained by different collaboration participants. In this case, the above-mentioned at least one sub-operation may generally include at least one query sub-operation for separately querying the data content corresponding to the above-mentioned several virtual data fields from the data maintained by different collaboration participants.

In an embodiment shown, if the above data query request is a request for performing data query calculation on the virtual data in the above virtual data set, in this case, the above at least one sub-operation can also be Include at least one calculation sub-operation for calculating the queried data.

For example, assuming that the above virtual data set is a user information table that stores users' personal information, this type of data query request can be "query the average age data of male users stored in the user information table" as described above. Data query request. In this case, in addition to the "query sub-operation for user information of male users in the user information table", the above at least one sub-operation may also include "user information for male users in the user information table" Filter calculation sub-operation for the age data in the message", and "Average calculation sub-operation for the filtered age data".

It should be noted that the type of at least one calculation sub-operation related to the data query request input by the user usually depends on the privacy calculation capability of the above-mentioned data collaboration platform itself, and is not specifically limited in this specification.

For example, in practical applications, the above-mentioned at least one calculation sub-operation can usually include a sub-operation for performing intersection calculation on data; a sub-operation for filtering and calculating data; a sub-operation for performing statistical calculation on data, etc. In this specification, No further enumeration will be given.

Step 206: Select at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy calculation, and generate the data query based on the at least one instantiation operator. Request the corresponding query process.

It should be noted that the operators described in this specification refer to various computing operations/computing functions involved in the process of privacy calculations on data maintained by each collaboration participant. That is to say, any computing operation/computing function involved in the process of privacy calculation for data maintained by each collaboration participant can be called an operator.

The above-mentioned instantiation operator refers to a computing function or computing operation based on a specific privacy computing technology. For example, the current mainstream privacy computing technologies usually include FL technology, MPC technology, and TEE technology, etc. Therefore, the instantiation operators in the above instantiation operator library can include instantiation operators implemented based on FL technology, Instantiation operators implemented based on MPC technology, operators implemented based on TEE technology, etc.

In practical applications, the privacy computing capability of the data collaboration platform itself is usually determined by the instantiation operator library managed by the data collaboration platform. The richer the types of instantiation operators contained in the instantiation operator library, the better. The more powerful the privacy computing capabilities of the data collaboration platform are.

In an embodiment shown, the instantiation operators in the instantiation operator library managed by the data collaboration platform can specifically be implemented based on the privacy computing technology supported by the data collaboration platform, and can also include operators based on each collaboration participation. Operator implemented by privacy computing technology supported by the party. In this way, the data collaboration platform can cover the private computing capabilities of each accessed collaboration participant.

After parsing the above data query request into at least one sub-operation, the server corresponding to the above-mentioned target collaboration participant can select at least one instantiation operator corresponding to the above-mentioned at least one sub-operation from the above-mentioned managed instantiation operator library. and generate a query process corresponding to the above data query request based on the selected at least one instantiation operator.

In an embodiment shown, the data collaboration platform can deploy a process planning component in a distributed manner on the server corresponding to the above-mentioned multiple collaboration participants. The process planning component can be used to manage the above-mentioned In the instantiation operator library, select at least one instantiation operator corresponding to the at least one sub-operation and generate a query process corresponding to the data query request based on the selected at least one instantiation operator. The instantiation operator library managed by the above-mentioned data collaboration platform can also be delivered to the corresponding server of each collaboration participant, and maintained by each collaboration participant respectively. In this case, after the server corresponding to the above-mentioned target collaboration participant parses the above-mentioned data query request into at least one sub-operation, the process planning component can be called from the locally maintained instantiation related to the privacy calculation. Select at least one instantiation operator corresponding to the at least one sub-operation from the operator library, and generate a query process corresponding to the above data query request based on the above at least one instantiation operator.

For example, please continue to refer to Figure 1. The above-mentioned process planning component may specifically include the data query planning component and the application execution planning component shown in Figure 1. In this case, the server corresponding to the above-mentioned target collaboration participant will add the above-mentioned After the data query request is parsed into at least one sub-operation, the above-mentioned data query planning component can be called first, and based on the parsed at least one above-mentioned sub-operation, a basic query process corresponding to the above-mentioned data query request is planned. Then the above-mentioned application execution planning component is called, and at least one instantiation operator corresponding to the above-mentioned at least one sub-operation is selected from the instantiation operator library managed by the data collaboration platform, and then based on the above-mentioned at least one instantiation operator, planning is performed with The instantiated query process corresponding to the above basic query process. At this time, the instantiated query process is a specific data collaboration application for querying data planned based on the data query requirements expressed by the data query request input by the user.

Among them, it should be noted that in actual applications, different data sources usually have different security requirements for the use of the data they maintain; for example, some data sources may prohibit the use of data for the purpose of privacy protection. Export the data domain in which it is located for use, or do not allow the data it maintains to be aggregated with data maintained by other data sources for use, etc. Therefore, in order to ensure that the selected instantiation operator can meet the security requirements of each collaboration participant for data use, you can refer to the security requirements of each collaboration participant for data usage and select the appropriate instantiation operator library from the above instantiation operator.

In an embodiment shown, when selecting at least one instantiation operator corresponding to at least one of the above sub-operations from the above-mentioned instantiation operator library, specifically, the data maintained by each collaboration participant may be first obtained. data security requirements, and then select at least one instantiation operator corresponding to at least one of the above sub-operations that meets the above data security requirements from the above-mentioned instantiation operator library.

In this way, even if the user does not understand the security requirements of the data used by each collaboration participant, the data collaboration platform can still automatically select for the user based on the user's data query needs that can satisfy the requirements of each collaboration participant. The instantiation operator of the security requirements for data usage can avoid query failures caused by data queries that do not meet the security requirements due to users not understanding the security requirements for data usage of each collaboration participant.

Among them, since the data security policy configured by each collaboration participant for the data maintained by each collaboration participant can, to a certain extent, reflect the security requirements of each collaboration participant for the use of data; therefore, when obtaining the data maintained by each collaboration participant, When meeting data security requirements, you can first obtain the data security policy configured by each collaboration participant for the data it maintains. After obtaining the data security policy configured by each collaboration participant for the data it maintains, you can based on the data security policy, To further determine the data security requirements of each collaboration participant for the data they maintain.

Among them, the specific content of the data security policy configured by each collaboration participant for the data maintained by each collaboration participant usually depends on the specific security requirements of each collaboration participant for data use, and therefore will not be specifically limited in this specification.

In an embodiment shown, the above-mentioned data security policy may specifically include one or a combination of one or more of the following: a security level configured for the data; a security protection level configured for the data; and other The data trust relationship of the collaboration participants; corresponding to the above data security policy, the security requirements of each collaboration participant for data use may also include one or a combination of the following: Based on the security Determined by the level, the security requirements are whether the data is allowed to be exported to the data domain where it is located; the security requirements are determined based on the security protection level, whether the data and the privacy calculation results of the data are allowed to be desensitized. ; Determine based on the data trust relationship, the security requirements of whether to allow the data to be aggregated with data maintained by other collaborative participants.

In an embodiment shown, corresponding data security requirements can also be configured for the instantiation operator library included in the instantiation operator library. The data security requirements configured for the instantiation operator library are specifically used to indicate the data security requirements that the instantiation operator can meet.

In this case, when selecting at least one instantiation operator corresponding to the at least one sub-operation that meets the data security requirements from the instantiation operator library, specifically, you can first search for at least one of the above-mentioned sub-operations in the instantiation operator library. At least one instantiation operator corresponding to a sub-operation; among the instantiation operators found at this time, there may be instantiation operators that do not meet the above security requirements. Then, the data security requirements of the data maintained by each collaborative participant can be matched with the found data security requirements corresponding to the at least one instantiation operator; and then based on the matching results, the data security requirements of the at least one instantiation operator can be calculated based on the matching results. The instantiation operator that matches the data security requirements of the data maintained by each of the above-mentioned collaboration participants is further selected.

Among them, it should be noted that, as mentioned above, the instantiation operators in the above-mentioned instantiation operator library can usually include instantiation operators implemented based on FL technology, instantiation operators implemented based on MPC technology, and instantiation operators based on Operators implemented by TEE technology, etc. However, instantiation operators implemented based on FL technology usually cannot be applied to data query scenarios. Therefore, in this specification, the instantiation operator corresponding to at least one of the above sub-operations may specifically include an instantiation operator implemented based on TEE and an instantiation operator implemented based on MPC.

For example, the instantiation operator corresponding to at least one of the above sub-operations may specifically include an instantiation operator implemented based on TEE for performing privacy query or privacy query calculation, and an instantiation operator implemented based on MPC for performing privacy query or privacy query calculation. instantiation operator.

In an embodiment shown, when generating a query process corresponding to the above data query request based on the at least one instantiation operator, multiple query processes can be generated based on the at least one instantiation operator, and then the query process can be generated based on the at least one instantiation operator. Multiple query processes are output and displayed to the user, so that the user can select an optimal query process corresponding to the above data query request from the multiple query processes. Of course, in actual applications, an optimal query process can be generated by default based on at least one of the above instantiation operators.

Step 208: Create a smart contract containing query logic corresponding to the query process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.

After the server corresponding to the above-mentioned target collaboration participant generates the query process corresponding to the above-mentioned data query request based on at least one of the above-mentioned instantiation operators, the server can further compile the query process into the form of a smart contract and deploy it to collaborate with the data In the blockchain network connected to the platform.

For example, in one example, the above-mentioned server can first automatically generate execution code related to the query logic corresponding to the above-mentioned query process, and compile the generated execution code into smart contract code; then, in the area connected to the above-mentioned data collaboration platform, A smart contract containing the smart contract code is deployed in the blockchain network. Among them, the specific process of deploying smart contracts will not be described in detail in this manual. For example, in practical applications, a smart contract creation transaction can be packaged based on the smart contract code, and the smart contract creation transaction can be published to the blockchain network. The node device in the blockchain network can perform consensus verification on the smart contract creation transaction, and after the consensus verification passes, execute the smart contract call transaction, create a smart contract account in the blockchain, and transfer the smart contract The smart contract code is anchored to the smart contract account to complete the creation of the smart contract.

In an embodiment shown, the data collaboration platform can deploy smart contract management components in a distributed manner on the servers corresponding to the above-mentioned multiple collaboration participants. The smart contract management components can be used to generate and Execution code related to the query logic corresponding to the query process, compile the generated execution code into smart contract code, and deploy the smart contract code containing the smart contract code in the blockchain network connected to the data collaboration platform Smart contracts.

In this case, after the server corresponding to the above-mentioned target collaboration participant generates the query process corresponding to the above-mentioned data query request based on the above-mentioned at least one instantiation operator, the server can call the smart contract management component to generate the query process corresponding to the above-mentioned data query request. Query the execution code related to the query logic corresponding to the query process, compile the generated execution code into smart contract code, and deploy the smart contract containing the smart contract code in the blockchain network connected to the data collaboration platform.

For example, please continue to refer to Figure 1. The above-mentioned smart contract management component may specifically include the application contract management component shown in Figure 1. In this case, the server corresponding to the above-mentioned target collaboration participant is based on at least one of the above-mentioned instantiations. After the operator generates the query process corresponding to the above data query request, it can call the application contract management component to generate execution code related to the query logic corresponding to the query process, and compile the generated execution code into a smart contract Code, deploy the smart contract containing the smart contract code in the blockchain network connected to the data collaboration platform.

In this specification, when the deployment of the above-mentioned smart contract is completed, the user can initiate a contract call for the smart contract through the client to complete the data query for the above-mentioned virtual data collection.

For example, please continue to refer to Figure 1. When the above smart contract is deployed, the user can initiate a contract call for the smart contract through the application execution client shown in Figure 1, and query the smart contract through the application execution client. The call result of the contract.

Please refer to Figure 4. Figure 4 is a flow chart of a data query method based on multi-party collaboration illustrated in this specification according to an exemplary embodiment. This method can be applied to the data collaboration platform shown in Figure 1; for example, the data collaboration The platform may specifically be a blockchain service platform used to manage the above-mentioned blockchain network; for example, a BaaS (Blockchain as a Service) platform; the method includes steps 402 to 408.

Step 402: Obtain a data query request for a virtual data set uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and input by the user; wherein the virtual data set is the data collaboration The platform performs data virtualization processing on the data maintained by the multiple collaborative participants for participating in privacy calculations to obtain a virtual data set.

When users who access the above-mentioned target collaboration participants through a client have data query needs, they can input a data query request for the virtual data table through the client to initiate a data query from a global data perspective. After the server corresponding to the above-mentioned target collaboration participant obtains the data query request input by the user through the client, the data query request can be uploaded to the above-mentioned data collaboration platform, and the data collaboration platform performs centralized processing.

Step 404: Parse the data query request and obtain at least one sub-operation related to the data query request.

Step 406: Select at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy calculation, and generate the data query based on the at least one instantiation operator. Request the corresponding query process.

Step 408: Create a smart contract containing query logic corresponding to the query process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform, so that the user can call the The query logic contained in the smart contract completes the data query for the virtual data collection.

After receiving the above-mentioned data query request uploaded by the above-mentioned target collaboration participant, the above-mentioned data collaboration platform can execute the execution logic shown in steps 404-408 and perform centralized processing of the data query request. Please refer to the figure for specific implementation details. The description of the embodiment shown in 2 will not be described in detail in this specification.

For example, through data virtualization processing technology, the data maintained by each collaborative participant is virtualized to obtain a virtual data collection. This not only enables full data fusion of multiple data sources, but also allows users to build on the virtual data collection based on the data virtualization processing technology. On the other hand, using data from a global data perspective does not require the data storage status of each collaboration participant, so the complexity of data use can be significantly reduced. By deploying the generated query process in the form of a smart contract on the blockchain for users to call, the characteristics of smart contracts can be fully utilized to make every query transparent, credible and easy to trace.

Moreover, because users can customize data query needs from a global data perspective based on virtual data collections obtained by virtual machine processing of data from multiple data sources, and based on the privacy computing capabilities of the data collaboration platform itself, Different data privacy query processes are planned according to the different data query needs of users, so that different data privacy query scenarios can be flexibly adapted, so that the data collaboration platform can activate more data usage scenarios. For example, different data sources often have different security requirements for the use of the data they maintain. As users with data usage needs, they usually do not understand the security requirements of each data source for data use, which may cause users to query and use multiple data sources from a global data perspective. When collecting data, there may be situations where the user's data usage requirements do not meet the security requirements of one or more data sources for data usage. Through the above technical solutions, since the data collaboration platform can flexibly adapt to users' data query needs based on its own privacy computing capabilities, the data collaboration platform's ability to adapt to data usage scenarios will be significantly improved. , so this also makes it possible for the data assistance platform to customize the query process for the user's data query needs that meets the security requirements of each data source for the use of data.

Corresponding to the foregoing method embodiments, this specification also provides embodiments of devices, electronic devices, and storage media.

FIG. 5 is a schematic structural diagram of an electronic device provided by an exemplary embodiment. Please refer to Figure 5. At the hardware level, the device includes a processor 502, an internal bus 504, a network interface 506, a memory 508 and a non-volatile memory 510. Of course, it may also include other hardware required for services. One or more embodiments of this specification may be implemented based on software. For example, the processor 502 reads the corresponding computer program from the non-volatile memory 510 into the memory 508 and then runs it. Of course, in addition to software implementation, one or more embodiments of this specification do not exclude other implementations, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each A logic unit can also be a hardware or logic device.

As shown in Figure 6, Figure 6 is a block diagram of a data query device based on multi-party collaboration according to an exemplary embodiment of this specification. This device can be applied to the electronic device shown in Figure 5 to implement this specification. technical solutions. The device 600 includes: a first acquisition module 601, a first parsing module 602, a first generation module 603 and a first deployment module 604.

Among them, the first acquisition module 601 acquires a data query request for a virtual data set input by the user; wherein the virtual data set is maintained by the data collaboration platform for the multiple collaboration participants for participating in privacy calculations. The data is a collection of virtual data obtained through data virtualization processing.

The first parsing module 602 parses the data query request and obtains at least one sub-operation related to the data query request.

The first generation module 603 selects at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy calculation, and generates the corresponding instantiation operator based on the at least one instantiation operator. Describe the query process corresponding to the data query request.

The first deployment module 604 creates a smart contract containing query logic corresponding to the query process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform for the user Make the call.

The specific details of each module of the above-mentioned device 600 have been described in detail in the previously described method flow, so they will not be described again here.

As shown in Figure 7, Figure 7 is a block diagram of another user service usage device shown in this specification according to an exemplary embodiment. This device can also be applied to the electronic device shown in Figure 5 to implement the instructions of this specification. Technical solutions. The device 700 includes: a second acquisition module 701, a second parsing module 702, a second generation module 703 and a second deployment module 704.

Among them, the second acquisition module 701 acquires the data query request for the virtual data set input by the user and uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants; wherein, the virtual The data set is a virtual data set obtained by performing data virtualization processing on the data maintained by the multiple collaboration participants for participating in privacy calculations by the data collaboration platform.

The second parsing module 702 parses the data query request to obtain at least one sub-operation related to the data query request.

The second generation module 703 selects at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy calculation, and generates the corresponding instantiation operator based on the at least one instantiation operator. Describe the query process corresponding to the data query request.

The second deployment module 704 creates a smart contract containing query logic corresponding to the query process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform for the user Make the call.

The specific details of each module of the above-mentioned device 700 have been described in detail in the previously described method flow, so they will not be described again here.

Correspondingly, this specification also provides an electronic device, which includes a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to implement all the previously described method flows. A step of.

Correspondingly, this specification also provides a computer-readable storage medium on which executable instructions are stored; wherein, when the instructions are executed by the processor, all the steps in the method flow described previously are implemented.

As for the device embodiment, since it basically corresponds to the method embodiment, please refer to the partial description of the method embodiment for relevant details. The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in One place, or it can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this specification. Persons of ordinary skill in the art can understand and implement the method without any creative effort.

The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, a laptop, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, or a game controller. desktop, tablet, wearable device, or a combination of any of these devices.

In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent storage in computer-readable media, random access memory (RAM) and/or non-volatile memory in the form of read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer-readable media includes both persistent and non-volatile, removable and non-removable media that can be implemented by any method or technology for storage of information. Information may be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium, can be used to store information that can be accessed by computing devices. As defined in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements not only includes those elements, but also includes Other elements are not expressly listed or are inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or device that includes the stated element.

The foregoing describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order shown, or sequential order, to achieve desirable results. Multitasking and parallel processing are also possible or may be advantageous in certain implementations.

The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the one or more embodiments of this specification. As used in one or more embodiments of this specification and the appended claims, the singular forms "a," "the" and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although one or more embodiments of this specification may use the terms first, second, third, etc. to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of this specification, the first information may also be called second information, and similarly, the second information may also be called first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "when" or "in response to determining."

The above are only preferred embodiments of one or more embodiments of this specification, and are not intended to limit one or more embodiments of this specification. Within the spirit and principles of one or more embodiments of this specification, Any modifications, equivalent substitutions, improvements, etc. shall be included in the scope of protection of one or more embodiments of this specification.

Claims

A data query method based on multi-party collaboration. A blockchain-based data collaboration platform connects multiple collaboration participants; the method is applied to services corresponding to any target collaboration participant among the multiple collaboration participants. end; the method includes:

Obtain a data query request for a virtual data set input by the user; wherein the virtual data set is data maintained by the data collaboration platform for participating in privacy calculations for the multiple collaboration participants, and performs data virtualization processing The obtained virtual data collection;

Parse the data query request and obtain at least one sub-operation related to the data query request;

Select at least one instantiation operator corresponding to the at least one sub-operation from a library of instantiation operators related to the privacy calculation, and generate an operator corresponding to the data query request based on the at least one instantiation operator. Query process;

Create a smart contract containing query logic corresponding to the query process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.
The method according to claim 1, wherein the data query request includes a data query statement;

Parse the data query request and obtain at least one sub-operation related to the data query request, including:

Perform syntax analysis on the data query statement to obtain a syntax tree corresponding to the data query statement;

At least one sub-operation related to the data query statement is determined based on the syntax tree.
The method of claim 2, wherein the data query statement includes an SQL statement.
The method according to claim 2, wherein the virtual data in the virtual data set includes several data fields; the several data fields are respectively maintained in different collaboration participants; and the at least one sub-operation includes selecting from all At least one query sub-operation for respectively querying the several data fields in the data maintained by the different collaboration participants.
The method according to claim 2, wherein the data query statement includes a request to perform data query calculation on the virtual data in the virtual data set; the at least one sub-operation further includes: performing calculation on the queried data. At least one computational sub-operation of .
The method of claim 5, wherein the at least one calculation sub-operation includes any one or a combination of more of the following:

Sub-operations for performing intersection calculations on data;

Sub-operations for filtering and calculating data;

Sub-operations that perform statistical calculations on data.
The method of claim 1, wherein at least one instantiation operator corresponding to the at least one sub-operation is selected from a library of instantiation operators related to the privacy computation, and based on the at least one instantiation The operator generates a query process corresponding to the data query request, including:

Obtain the data security requirements of the data maintained by each collaboration participant, and select at least one instantiation corresponding to the at least one sub-operation that satisfies the data security requirements from the instantiation operator library related to the privacy calculation. operator, and generate a query process corresponding to the data query request based on the at least one instantiated operator.
The method according to claim 7, wherein the instantiation operator includes an operator implemented based on the privacy computing technology supported by the data collaboration platform and the collaboration participants.
The method according to claim 1, wherein obtaining the data security requirements of data maintained by each collaboration participant includes:

Obtain the data security policy configured by each collaboration participant for the data it maintains;

Data security requirements for data maintained by each collaboration participant are determined based on the data security policy.
The method of claim 9, wherein

The data security policy includes one or a combination of the following:

The security level configured for the data;

The security protection level configured for the data;

Data trust relationships with other collaboration participants;

The data security requirements include one or a combination of the following:

Determined based on the security level, whether the data is allowed to be exported to the security requirements of the data domain where it is located;

Determine based on the security protection level, the security requirements of whether to allow data desensitization processing on the data and the privacy calculation results for the data;

Based on the data trust relationship, it is determined whether the security requirements are allowed to aggregate the data with data maintained by other collaboration participants.
The method according to claim 1, wherein the data collaboration platform distributes process planning components on servers corresponding to the multiple collaboration participants; services corresponding to the multiple collaboration participants The instantiation operator library is maintained separately on the terminal;

Select at least one instantiation operator corresponding to the at least one sub-operation from a library of instantiation operators related to the privacy calculation, and generate an operator corresponding to the data query request based on the at least one instantiation operator. Inquiry process, including:

Call the process planning component, select at least one instantiation operator corresponding to the at least one sub-operation from the locally maintained instantiation operator library related to the privacy calculation, and perform the calculation based on the at least one instantiation operator. Generate a query process corresponding to the data query request.
The method according to claim 11, wherein the instantiation operator library includes several instantiation operators and data security requirements corresponding to the instantiation operators;

Selecting at least one instantiation operator corresponding to the at least one sub-operation that meets the data security requirements from the instantiation operator library includes:

Search for at least one instantiation operator corresponding to the at least one sub-operation in the instantiation operator library;

Match the data security requirements of the data maintained by each collaboration participant with the data security requirements corresponding to the found at least one instantiation operator;

Based on the matching result, an instantiation operator matching the data security requirements of the data maintained by the respective collaboration participants is selected from the at least one instantiation operator.
The method according to claim 12, wherein the instantiation operator includes one or a combination of multiple items shown below:

Instantiation operator implemented based on TEE;

Instantiation operator implemented based on MPC.
The method according to claim 1, wherein generating a query process corresponding to the data query request based on the at least one instantiation operator includes:

Generate multiple query processes based on the at least one instantiation operator;

The multiple query processes are output and displayed to the user, so that the user selects the query process corresponding to the data query request from the multiple query processes.
The method according to claim 1, wherein the data collaboration platform deploys smart contract management components in a distributed manner on the server corresponding to the multiple collaboration participants;

Create a smart contract containing query logic corresponding to the query process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform, including:

Call the smart contract management component to generate execution code related to the query logic corresponding to the query process, and compile the generated execution code into smart contract code;

Deploy a smart contract containing the smart contract code in a blockchain network connected to the data collaboration platform.
The method of claim 15, wherein the multiple collaboration participants include multiple data centers distributed in different regions.
The method according to claim 1, wherein the data collaboration platform includes a blockchain service platform corresponding to the blockchain network.
A data query method based on multi-party collaboration, the method is applied to a data collaboration platform based on blockchain; wherein the data collaboration platform connects multiple collaboration participants; the method includes:

Obtain a data query request for a virtual data set uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and input by the user; wherein the virtual data set is the data query request for the virtual data set by the data collaboration platform. Describes the data maintained by multiple collaborative participants for participating in privacy calculations, and a virtual data set obtained by performing data virtualization processing;

Parse the data query request and obtain at least one sub-operation related to the data query request;

Select at least one instantiation operator corresponding to the at least one sub-operation from a library of instantiation operators related to the privacy calculation, and generate an operator corresponding to the data query request based on the at least one instantiation operator. Query process;

Create a smart contract containing query logic corresponding to the query process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.
A data query device based on multi-party collaboration. The blockchain-based data collaboration platform connects multiple collaboration participants; the device is applied to services corresponding to any target collaboration participant among the multiple collaboration participants. end; the device includes:

The first acquisition module acquires a data query request for a virtual data set input by the user; wherein the virtual data set is data maintained by the data collaboration platform for the multiple collaboration participants for participating in privacy calculations, Virtual data collection obtained through data virtualization processing;

The first parsing module parses the data query request and obtains at least one sub-operation related to the data query request;

The first generation module selects at least one instantiation operator corresponding to the at least one sub-operation from the instantiation operator library related to the privacy computation, and generates the corresponding instantiation operator based on the at least one instantiation operator. The query process corresponding to the data query request;

The first deployment module creates a smart contract containing query logic corresponding to the query process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform for the user to perform transfer.
A data query device based on multi-party collaboration, the device is applied to a data collaboration platform based on blockchain; wherein the data collaboration platform connects multiple collaboration participants; the device includes:

The second acquisition module acquires the data query request for the virtual data set input by the user and uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants; wherein the virtual data set is The data collaboration platform performs data virtualization processing on the data maintained by the multiple collaboration participants for participating in privacy calculations to obtain a virtual data set;

The second parsing module parses the data query request and obtains at least one sub-operation related to the data query request;

The second generation module selects at least one instantiation operator corresponding to the at least one sub-operation from a library of instantiation operators related to the privacy calculation, and generates the corresponding sub-operation based on the at least one instantiation operator. The query process corresponding to the data query request;

The second deployment module creates a smart contract containing query logic corresponding to the query process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform for the user to perform transfer.
An electronic device including:

processor;

Memory used to store instructions executable by the processor;

Wherein, the processor implements the method according to any one of claims 1-18 by running the executable instructions.
A computer-readable storage medium having computer instructions stored thereon, which when executed by a processor, implements the steps of the method according to any one of claims 1-18.