WO2024016549A1

WO2024016549A1 - Multi-party collaboration-based privacy computing method and apparatus

Info

Publication number: WO2024016549A1
Application number: PCT/CN2022/135217
Authority: WO
Inventors: 马超; 王天雨; 孙善禄; 杨仁慧
Original assignee: 蚂蚁区块链科技(上海)有限公司
Priority date: 2022-07-22
Filing date: 2022-11-30
Publication date: 2024-01-25
Also published as: CN115270202A

Abstract

One or more embodiments of the present description provide a multi-party collaboration-based privacy computing method and apparatus. The method comprises: acquiring a computing process, specified by a user, of performing privacy computing on data maintained by at least some collaborative participants among a plurality of collaborative participants, wherein the computing process comprises a computing process specified by the user and composed of at least one functional operator related to privacy computing; acquiring a data security requirement of the at least some collaborative participants on the data maintained by said collaborative participants, selecting, from an instantiated operator library related to privacy computing, at least one instantiated operator satisfying the data security requirement and corresponding to the at least one functional operator, and on the basis of the at least one instantiated operator, generating an instantiated computing process corresponding to the computing process; and creating a smart contract containing a privacy computing logic corresponding to the instantiated computing process, and deploying the created smart contract into a blockchain network connected to a data collaboration platform.

Description

A privacy computing 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 privacy computing 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 privacy computing 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; wherein, the plurality of collaboration participants respectively maintain data for participating in privacy calculations; the method includes: obtaining the user-specified data maintained by at least some of the collaboration participants among the plurality of collaboration participants. The calculation process of performing privacy calculation on the data; wherein the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation; obtaining at least part of the collaboration participants for which According to the data security requirements of the maintained data, at least one instantiation operator corresponding to the at least one functional operator that meets the data security requirements is selected from the instantiation operator library related to the privacy calculation, and based on The at least one instantiation operator generates an instantiation calculation process corresponding to the calculation process; creates a smart contract containing privacy calculation logic corresponding to the instantiation calculation process, and deploys the created smart contract to the The data collaboration platform is connected to the blockchain network for the user to call.

This specification also proposes a privacy computing 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 multiple collaboration participants Data used to participate in privacy calculations are respectively maintained; the method includes: obtaining the user-specified data uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants. The calculation process of performing privacy calculation on at least part of the data maintained by the collaborative participants; wherein the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation; obtaining the According to the data security requirements of at least some of the collaboration participants for the data they maintain, at least one corresponding to the at least one functional operator that meets the data security requirements is selected from the instantiation operator library related to the privacy calculation. An instantiation operator, and generate an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator; create a smart contract containing privacy calculation logic corresponding to the instantiation calculation process, and create The smart contract is deployed into the blockchain network connected to the data collaboration platform for the user to call.

This specification also proposes a privacy computing 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; wherein, the multiple collaboration participants respectively maintain data for participating in privacy calculations; the device includes: a first acquisition module, which acquires the user-specified data for the multiple collaboration participants. A calculation process for performing privacy calculation on at least part of the data maintained by the collaborative participants; wherein the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation; the first generation module , obtain the data security requirements of at least some of the collaborative participants for the data they maintain, and select the at least one functional operator that meets the data security requirements from the instantiation operator library related to the privacy calculation. corresponds to at least one instantiation operator, and generates an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator; the first deployment module creates a privacy calculation corresponding to the instantiation calculation 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 privacy computing 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 multiple collaboration participants The data used to participate in privacy calculations are respectively maintained; the device includes: a second acquisition module, which acquires the data uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and specified by the user. A calculation process for performing privacy calculation on data maintained by at least some of the multiple collaboration participants; wherein the calculation process includes at least one functional operator specified by the user and related to the privacy calculation. Calculation process; the second generation module obtains the data security requirements of at least some of the collaborative participants for the data they maintain, and selects from the instantiation operator library related to the privacy calculation that satisfies the data security requirements. At least one instantiation operator corresponding to the at least one functional operator, and based on the at least one instantiation operator, an instantiation calculation process corresponding to the calculation process is generated; the second deployment module creates the Calculate the smart contract corresponding to the privacy calculation logic of the process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.

In the technical solution above in this specification, not only can the calculation process of privacy calculation be more transparent, credible and easy to trace when performing privacy calculation on data from multiple data sources; moreover, it can also be used when the user does not understand each collaboration participant Based on the data collaboration platform's own privacy computing capabilities and the different computing needs of users, we plan different instantiation calculations that can meet the security requirements of each collaboration participant for data use, based on the security requirements of each party for data use. process, so that the data collaboration platform can flexibly adapt to different data privacy scenarios and activate more private computing 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 flowchart of a privacy calculation method based on multi-party collaboration illustrated in this specification according to an exemplary embodiment.

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 privacy calculation 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 privacy computing device based on multi-party collaboration according to an exemplary embodiment of this specification.

FIG. 7 is a block diagram of another privacy computing 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. Based on the privacy computing capabilities of the data collaboration platform itself, the user does not understand the security requirements of each collaboration participant for data use. Under the premise, we can flexibly plan technical solutions for instantiated computing processes corresponding to user-customized computing processes that meet the security requirements of each data source.

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.

For any target collaboration participant among the multiple collaboration participants, a user who accesses the server corresponding to the target collaboration participant has data maintained for at least some of the collaboration participants among the multiple collaboration participants. When performing calculation requirements for privacy calculations, you can specify a calculation process corresponding to your own calculation requirements based on the functional operators in the functional operator library related to privacy calculations.

The server corresponding to the target collaborator, after obtaining the calculation process specified by the user, can further obtain the data security requirements of at least some of the collaboration participants for the data it maintains, from the instantiation related to privacy calculations Select at least one instantiation operator corresponding to the at least one functional operator that meets the data security requirements from the operator library, and generate an instantiation calculation process corresponding to the above calculation process based on the at least one instantiation operator.

Then, a smart contract containing the privacy calculation logic corresponding to the instantiation calculation process can be created, and the created smart contract can be deployed to the blockchain network connected to the data collaboration platform for the user Make the call.

In the above technical solution, not only can the calculation process of privacy calculation be more transparent, credible and easy to trace when performing privacy calculation on data from multiple data sources; moreover, it can also be used when the user does not understand the data of each collaborative participant. Under the premise of the security requirements of use, based on the privacy computing capabilities of the data collaboration platform itself, according to the different computing needs of users, different instantiation computing processes that can meet the security requirements of each collaboration participant for data use are planned, so as to This allows the data collaboration platform to flexibly adapt to different data privacy scenarios and activate more privacy computing 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 may include the following components, for example.

The application execution planning component is used to select at least one functional operator included in the user-specified calculation process from the instantiated operator library managed by the data collaboration platform based on the data security requirements of the data maintained by each collaboration participant. At least one instantiation operator corresponding to the child, and then based on the above at least one instantiation operator, plan an instantiation calculation process corresponding to the above calculation process that meets the above data security requirements of each collaboration participant. At this time, the instantiated computing process is a specific data collaboration application for privacy computing planned based on the user-specified computing process.

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

The execution scheduling component is used to interact with the above-mentioned smart contracts. By monitoring the call results of the smart contracts stored on the blockchain, it further calls the local instantiation operator to complete the relevant privacy calculations and submits the calculation results to The above smart contract.

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 use the application to execute the client and specify the data corresponding to their own privacy computing needs based on the functional operators managed by the data collaboration platform. In the calculation process, the execution client can also be used to initiate a contract call for the above-mentioned smart contract, and the application execution client can be used to query the call result of the smart contract.

Please refer to Figure 2. Figure 2 is a flow chart of a privacy calculation 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 206.

Step 202: Obtain the user-specified calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants; wherein the calculation process includes the user-specified calculation process and the privacy calculation process. A calculation process composed of at least one related functional operator.

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.

The operators described in this manual refer to various calculation operations/calculation functions involved in the process of privacy calculation for 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 functional operator refers to a computing function or computing operation to be implemented. Any computing operation/computing function to be implemented in the process of performing privacy calculations on data maintained by each collaboration participant can be called a functional operator. The above-mentioned instantiation operator refers to a computing function or computing operation that has been implemented 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.

For another example, take the above functional operator as "intersection calculation" as an example, and the calculation function to be implemented at this time is "intersection calculation", then the instantiation operator corresponding to the functional operator can specifically include The instantiation operator for intersection calculation implemented by MPC technology, the instantiation operator for intersection calculation based on TEE technology, etc. That is to say, for the intersection calculation function, two instantiation operators can be implemented based on MPC technology and TEE technology respectively.

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. In this specification, 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 privacy computing 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.

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.

After completing the data query, users can specify the calculation process corresponding to their own privacy calculation needs based on the functional operators managed by the data collaboration platform.

Among them, it should be noted that the specific content of the above calculation process usually depends on the user's privacy calculation needs, and is not specifically limited in this specification. In practical applications, users can use the functions in the above functional operator library to Operators can be combined in any form to customize the calculation process corresponding to your own calculation needs. Among them, users can specify the calculation process operations corresponding to their own privacy calculation needs based on the functional operators managed by the data collaboration platform, which can be completed on the client.

For example, assume that the above virtual data set is a user information table that stores user personal information. The information table includes gender fields and age fields. In this scenario, if the user's privacy calculation requirement for the user information table is to "query the average age data of male users stored in the user information table", then at this time the user can choose from the above functional operator library The functional operator corresponding to the calculation function "query and calculate the user information of male users in the user information table" and "screening and calculation of age data in the user information of male users in the user information table" The functional operator corresponding to this calculation function, and the functional operator corresponding to the calculation function "average calculation sub-operation for filtered age data" are then customized based on the three selected functional operators. The calculation process corresponding to its own privacy calculation needs.

For another example, if the user's privacy calculation requirement for the user information table is to "train a machine learning model based on the user data in the user information table", then the user can select "Machine Learning Training" in the above functional operator library The functional operator corresponding to this computing function can be used to customize the computing process corresponding to its own privacy computing needs.

In an embodiment shown, the server corresponding to the above-mentioned target collaboration participant can specifically output the functional operator library related to privacy calculation managed by the data collaboration platform to the user through the client, and Obtain at least one functional operator selected by the user in the output functional operator library, and then generate privacy calculations based on the at least one functional operator based on the data maintained by at least some of the multiple collaboration participants. calculation process.

In an implementation shown, the server corresponding to the above-mentioned target collaboration participant can specifically output a calculation process planning interface to the user through the client; wherein, the calculation process planning interface can include the functional operator library . Users can interact with the client through the computing process planning interface to express their own privacy computing needs. For example, the user can operate in the calculation process planning interface, select at least one functional operator from the functional operator library, and specify the logical sequence for the at least one functional operator in the calculation process planning interface. Express your computing needs to the client. For example, in one example, the user can form a DAG (Directed Acyclic Graph) by specifying a one-way logical sequence for at least one of the above functional operators in the calculation process planning interface. Acyclic graph) graphic structure.

Among them, it should be noted that the user's operation method in the calculation process planning interface is not particularly limited in this manual. For example, in one example, the functional operators included in the functional operator library can be input to the user in the form of options through the above-mentioned calculation process planning interface, and the user can select at least one functional operator from it, and perform the calculation in the above-mentioned calculation process. In the process planning interface, one-way connections are set between the selected functional operators to express the logical sequence between the functional operators, and then based on the one-way logical sequence, at least one of the above functional operators is Concatenate them into a DAG graphic structure to serve as your own designated calculation process.

Step 204: Obtain the data security requirements of at least some of the collaboration participants for the data they maintain, and select the at least one functionality that meets the data security requirements from the instantiation operator library related to the privacy calculation. At least one instantiation operator corresponding to the operator, and based on the at least one instantiation operator, an instantiation calculation process corresponding to the calculation process is generated.

The server corresponding to the above-mentioned target collaboration participant, after obtaining the user-specified calculation process for performing privacy calculations on the data maintained by at least some of the above-mentioned multiple collaboration participants, can calculate from the above-mentioned instantiation managed In the sub-library, select at least one instantiation operator corresponding to the above-mentioned at least one functional operator, and generate an instantiation query process corresponding to the above-mentioned calculation process based on the selected at least one instantiation operator.

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 this case, after the server corresponding to the above-mentioned target collaboration participant obtains the user-specified calculation process for performing privacy calculations on the data maintained by at least some of the above-mentioned multiple collaboration participants, it can specifically First, obtain the data security requirements of the data maintained by each collaboration participant, and then select at least one instantiation operator corresponding to at least one of the above functional operators 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 make selections for users based on the user's privacy computing needs that meet the requirements of each collaboration participant. Instantiation operator for security requirements of data usage.

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: determining whether to allow the use of data based on the security level. Derive the security requirements of the data domain in which it is located; the security requirements determined based on the security protection level, whether to allow data desensitization processing on the data and the privacy calculation results of the data; determine based on the data trust relationship Security requirements to allow aggregation of data 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, you can first search for at least one instantiation operator corresponding to at least one of the above functional operators in the instance operator library; at this time, there may be some of the instantiation operators found that do not meet the above requirements. Instantiation operator for security requirements.

Then, the data security requirements of at least some of the above collaborative participants for the data they maintain 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 above at least one instance can be An instantiation operator is further selected from the operators that matches the data security requirements of at least some of the above collaboration participants for the data they maintain.

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 obtain at least part of the above-mentioned According to the data security requirements of the data maintained by the collaboration participants, at least one instantiation operator corresponding to the above-mentioned at least one functional operator that meets the above-mentioned data security requirements is selected from the managed instantiation operator library, and based on the above-mentioned At least one instantiation operator generates an instantiation calculation process corresponding to the calculation process. The instantiation operator library managed by the above 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 obtaining the calculation process specified by the user, the server corresponding to the above-mentioned target collaboration participant can call the process planning component to obtain the data security requirements of at least some of the above-mentioned collaboration participants for the data they maintain, From the locally maintained instantiation operator library related to the above-mentioned privacy calculation, select at least one instantiation operator corresponding to the above-mentioned at least one functional operator that meets the above-mentioned data security requirements, and based on the above-mentioned at least one instantiation operator Generate an instantiated query process corresponding to the above calculation process.

For example, please continue to refer to Figure 1. The above process planning component may include the application execution planning component shown in Figure 1. At this time, the above-mentioned instantiation calculation process is a specific data collaboration application for privacy calculation planned based on the user-specified calculation process composed of functional operators.

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

Step 206: Create a smart contract containing privacy computing logic corresponding to the instantiation calculation process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user Make the call.

After the server corresponding to the above-mentioned target collaboration participant generates the above-mentioned instantiation calculation process based on the above-mentioned at least one instantiation operator, the server can further compile the instantiation calculation process into the form of a smart contract and deploy it to interface with the data collaboration platform. in the blockchain network.

For example, in one example, the above-mentioned server can first automatically generate execution code related to the privacy calculation logic corresponding to the above-mentioned instantiation calculation process, and compile the generated execution code into smart contract code; then, in conjunction with the above-mentioned data collaboration platform A smart contract containing the smart contract code is deployed in the connected 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 server corresponding to the above-mentioned multiple collaboration participants. The smart contract management components can be used to generate and The above-mentioned instantiation calculation process corresponds to the execution code related to the privacy calculation logic, compiles the generated execution code into smart contract code, and deploys 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 above-mentioned instantiation calculation process based on the above-mentioned at least one instantiation operator, the server can call the smart contract management component to further generate the above-mentioned instantiation calculation process. Corresponding privacy calculation logic related execution code, 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 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 privacy calculation for the data maintained by at least some of the above collaborative participants.

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.

In an embodiment shown, the privacy calculation logic corresponding to the above-mentioned instantiation calculation process may specifically be a calculation scheduling logic corresponding to the instantiation calculation process; wherein, the calculation scheduling logic is specifically used to target at least one of the above-mentioned instantiation calculation processes. The instantiation operator performs calculation scheduling to complete privacy calculations for the data maintained by at least some of the above collaborative participants.

In this case, the above-mentioned smart contract may specifically be a smart contract that performs calculation and scheduling for the above-mentioned privacy calculation logic. In this smart contract, in addition to maintaining the calculation scheduling sequence corresponding to each instantiation operator in the above-mentioned instantiation calculation process, it also needs to maintain the calculation status corresponding to each above-mentioned instantiation operator. This calculation status is used to represent each Whether the instantiation operator completes the calculation.

After the user triggers the call to the smart contract through the above-mentioned client, the smart contract can determine the instantiation operator that needs to be called first according to the calculation scheduling sequence, and then generate a calculation event corresponding to the instantiation operator. The execution scheduling component on the server where the instantiation operator is located, after listening to the calculation event, can further call the instantiation operator to perform data privacy calculations, and after the calculation is completed, the calculation results are mentioned to the smart contract. . After the smart contract receives the calculation result, it will trigger the calculation status of the instantiated operator to be updated to the completed calculation status. After the instantiated calculation status is updated to the completed calculation status, the smart contract will continue to determine the next instantiated operator that needs to be called, and execute the same execution process as mentioned above, and so on. Until each instantiation operator in the above instantiation calculation process, the data privacy calculation is completed in sequence according to the calculation scheduling order.

Please refer to Figure 4. Figure 4 is a flow chart of a privacy calculation 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 406.

Step 402: Obtain the user-specified data uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and maintained for at least some of the collaboration participants to perform privacy calculations. The calculation process; wherein the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation.

In this embodiment, the server corresponding to the above-mentioned target collaboration participant, after obtaining the user-specified calculation process for performing privacy calculations on the data maintained by at least part of the above-mentioned multiple collaboration participants, can The data query request is uploaded to the above-mentioned data collaboration platform, which performs centralized processing.

Step 404: Obtain the data security requirements of at least some of the collaboration participants for the data they maintain, and select from the instantiation operator library related to the privacy calculation the at least one functionality that satisfies the data security requirements. At least one instantiation operator corresponding to the operator, and based on the at least one instantiation operator, an instantiation calculation process corresponding to the calculation process is generated.

Step 406: Create a smart contract containing privacy calculation logic corresponding to the instantiation calculation process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user Make the call.

After receiving the above-mentioned instantiation calculation process uploaded by the above-mentioned target collaboration participant, the above-mentioned data collaboration platform can execute the execution logic shown in steps 404-406, and perform centralized processing on the instantiation calculation process. For specific implementation details, please Referring to the description of the embodiment shown in FIG. 2 , no detailed description will be given in this specification.

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.

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, on the premise that users do not understand the security requirements of each collaboration participant for data use, based on the privacy computing capabilities of the data collaboration platform itself, different computing needs can be planned to meet the needs of each collaboration participant. The instantiation calculation process for the security requirements of data usage allows the data collaboration platform to flexibly adapt to different data privacy scenarios and activate more privacy computing 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. This makes it difficult for users to plan a set of data that perfectly meets the needs of each data source based on their own privacy computing needs. Calculation procedures for security requirements for the use of data.

Through the above technical solutions, because the data collaboration platform's own privacy computing capabilities can be used as a reference, the security requirements of each data source for data use can be used to automatically plan for users to meet the security requirements of each collaboration participant for data use. For the required instantiation calculation process, users no longer need to pay attention to the security requirements of each data source for the use of data. For the data collaboration platform, its adaptability to privacy computing scenarios will also be significantly improved. , so this makes it possible for the data assistance platform to activate more data privacy computing scenarios that can meet the security requirements of different data sources.

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 privacy computing 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 that acquires a user-specified calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants; wherein the calculation process includes: The calculation process composed of at least one functional operator specified by the user and related to the privacy calculation; the first generation module 602 obtains the data security requirements of at least some of the collaborative participants for the data they maintain, from the data security requirements related to the privacy calculation. Select at least one instantiation operator corresponding to the at least one functional operator that meets the data security requirements from a library of instantiation operators related to privacy computing, and generate the corresponding instantiation operator based on the at least one instantiation operator. The instantiation calculation process corresponding to the calculation process; the first deployment module 603 creates a smart contract containing the privacy calculation logic corresponding to the instantiation calculation process, and deploys the created smart contract to the data collaboration platform. in the blockchain network for the user to make calls.

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, which acquires the information uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and specified by the user for at least part of the multiple collaboration participants. The calculation process of performing privacy calculation on data maintained by collaborative participants; wherein the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation; the second analysis module 702, Obtain the data security requirements of at least some of the collaborative participants for the data they maintain, and select from the instantiation operator library related to the privacy calculation that satisfies the data security requirements and corresponds to the at least one functional operator. At least one instantiation operator, and generate an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator; the second generation module 703 creates a privacy calculation corresponding to the instantiation calculation 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.

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), and 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 privacy computing 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; wherein, the multiple collaboration participants respectively maintain data used to participate in privacy calculations; the method includes:

Obtaining a user-specified calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants; wherein the calculation process includes at least a user-specified calculation process related to the privacy calculation. A calculation process composed of functional operators;

Obtain the data security requirements of at least some of the collaborative participants for the data they maintain, and select from the instantiation operator library related to the privacy calculation that satisfies the data security requirements and corresponds to the at least one functional operator. at least one instantiation operator, and generate an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator;

Create a smart contract containing privacy calculation logic corresponding to the instantiation calculation 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 obtaining a user-specified calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants includes:

Output the function operator library related to the privacy calculation to the user through the user client corresponding to the target collaboration participant, and obtain at least one function selected by the user in the output function operator library A functional operator, based on the at least one functional operator, generates a calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants.
The method according to claim 2, wherein the function operator library is output to the user through a user client corresponding to the target collaboration participant, and the function operator library output by the user is obtained. At least one functional operator selected in, based on the at least one functional operator, generates a calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants, including:

A computing process planning interface is output to the user through a user client corresponding to the target collaboration participant; wherein the computing process planning interface includes the functional operator library;

Obtain at least one functional operator selected by the user from the functional operator library in the calculation process planning interface, and the logic specified for the at least one functional operator in the calculation process planning interface order;

A calculation process for performing privacy calculations on data maintained by at least part of the collaboration participants among the plurality of collaboration participants is generated based on the at least one functional operator and the logical sequence specified for the at least one functional operator.
The method of claim 3, wherein the calculation process includes a DAG graph structure composed of the at least one functional operator.
The method of claim 1, wherein obtaining the data security requirements of at least some of the collaboration participants for the data they maintain includes:

Obtaining the data security policy configured by at least some of the collaboration participants for data maintained by them;

Data security requirements for data maintained by the at least some collaboration participants are determined based on the data security policy.
The method of claim 5, 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;

Obtain the data security requirements of at least some of the collaborative participants for the data they maintain, and select from the instantiation operator library related to the privacy calculation that satisfies the data security requirements and corresponds to the at least one functional operator. At least one instantiation operator, and generating an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator, including:

Call the process planning component, obtain the data security requirements of at least some of the collaboration participants for the data they maintain, and select from the locally maintained instantiation operator library that satisfies the data security requirements with the at least one At least one instantiation operator corresponding to the functional operator, and based on the at least one instantiation operator, an instantiation calculation process corresponding to the calculation process is generated.
The method according to claim 7, 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 functional operator 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 functional operator in the instantiation operator library;

Match the data security requirements of at least some of the collaboration participants for the data they maintain with the data security requirements corresponding to the found at least one instantiation operator;

Based on the matching result, an instantiation operator is selected from the at least one instantiation operator that matches the data security requirements of the data maintained by the at least part of the collaboration participants.
The method of claim 8, wherein the instantiation operator includes an operator implemented based on privacy computing technology supported by the data collaboration platform and the collaboration participants.
The method according to claim 9, 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;

Instantiation operator implemented based on TL.
The method according to claim 1, wherein generating an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator includes:

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

The plurality of instantiated computing processes are output and displayed to the user, so that the user selects an instantiated computing process corresponding to the computing process from the plurality of instantiated computing 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 privacy computing logic corresponding to the instantiation calculation 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 privacy calculation logic corresponding to the instantiation calculation 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 according to claim 12, wherein the privacy calculation logic corresponding to the instantiation calculation process includes calculation scheduling logic corresponding to the instantiation calculation process; wherein the calculation scheduling logic is used for the At least one instantiation operator performs calculation scheduling to complete privacy calculations for data maintained by at least part of the collaboration participants.
The method of claim 1, 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 privacy computing 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 multiple collaboration participants respectively maintain user For data participating in privacy calculations; the methods include:

Obtain the calculation process of privacy calculation for the data maintained by at least part of the multiple collaboration participants uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and specified by the user ; Wherein, the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation;

Obtain the data security requirements of at least some of the collaborative participants for the data they maintain, and select from the instantiation operator library related to the privacy calculation that satisfies the data security requirements and corresponds to the at least one functional operator. at least one instantiation operator, and generate an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator;

Create a smart contract containing privacy calculation logic corresponding to the instantiation calculation process, and deploy the created smart contract to the blockchain network connected to the data collaboration platform for the user to call.
A privacy computing device based on multi-party collaboration. A 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; wherein, the multiple collaboration participants respectively maintain data used to participate in privacy calculations; the device includes:

The first acquisition module obtains a user-specified calculation process for performing privacy calculations on data maintained by at least some of the multiple collaboration participants; wherein the calculation process includes the calculation process specified by the user and the A calculation process composed of at least one functional operator related to privacy calculations;

The first generation module obtains the data security requirements of at least some of the collaborative participants for the data they maintain, and selects the at least one operator that satisfies the data security requirements from the instantiation operator library related to the privacy calculation. At least one instantiation operator corresponding to the functional operator, and generating an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator;

The first deployment module creates a smart contract containing privacy calculation logic corresponding to the instantiation calculation process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform for all The user makes the call.
A privacy computing 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 multiple collaboration participants respectively maintain user For data participating in privacy calculations; the device includes:

The second acquisition module obtains the data uploaded by the corresponding server of any target collaboration participant among the multiple collaboration participants and specified by the user and maintained for at least some of the collaboration participants. The calculation process of privacy calculation; wherein the calculation process includes a calculation process composed of at least one functional operator specified by the user and related to the privacy calculation;

The second generation module obtains the data security requirements of at least some of the collaborative participants for the data they maintain, and selects the at least one operator that satisfies the data security requirements from the instantiation operator library related to the privacy calculation. At least one instantiation operator corresponding to the functional operator, and generating an instantiation calculation process corresponding to the calculation process based on the at least one instantiation operator;

The second deployment module creates a smart contract containing privacy calculation logic corresponding to the instantiation calculation process, and deploys the created smart contract to the blockchain network connected to the data collaboration platform for all The user makes the call.
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 1-16 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 1-16.