Data processing method, device, computer equipment and medium for privacy data

Abstract

The present invention discloses a data processing method, device, computer equipment and medium for privacy data, including: obtaining a local registration algorithm instance list by obtaining local environmental parameters and the environmental parameters of each participant, and sending a registration algorithm instance instance list acquisition request to each participant, when receiving the registration algorithm instance list fed back by each participant, calculating the intersection of the local registration algorithm instance list and all received registration algorithm instance lists, and taking the registration algorithm instances in the obtained intersection as common algorithm instances, based on the preset weight corresponding to each common algorithm instance, the local environmental parameters and the environmental parameters of each participant, from all the common algorithm instances, screening out a target algorithm instance, and using the target algorithm instance to perform the data processing task of privacy data. The present invention can improve the efficiency of the data processing task of privacy data.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, computer equipment and medium for processing private data. Background Art

[0002] As data becomes increasingly important, protecting the privacy of user data is an important prerequisite for the country to coordinate data resources. Institutions and individuals are becoming more and more sensitive to data privacy. How to share the value of data while ensuring privacy has become a major concern for people, such as secure auctions, secure elections, and privacy data comparison. If this problem cannot be solved, the data will become a personal collection and will be deposited in the local database of each institution or individual, unable to play its due value. Multi-party secure computing technology is an important technology to solve this problem.

[0003] Multi-party secure computation (MPC: Secure Muti-Party Computation) is the calculation of ciphertext through cryptographic means, and then returns the calculation results to the participants.

[0004] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art: the existing solution adopts a solidified algorithm, which can protect privacy data and perform data calculations, but for data processing between privacy data, if a solidified algorithm is used, there will be other more efficient algorithms of the same type in certain scenarios, and it is impossible to select a specific algorithm for the scenario, resulting in low efficiency in processing privacy data. Summary of the invention

[0005] Embodiments of the present invention provide a method, apparatus, computer device and storage medium for processing privacy data, so as to improve the efficiency of processing privacy data.

[0006] In order to solve the above technical problems, the present application provides a method for processing private data, including:

[0007] Obtain local environment parameters and environment parameters of each participant;

[0008] Obtain a local registered algorithm instance list, and send a request to obtain the registered algorithm instance list to each of the participants;

[0009] When receiving the registration algorithm instance list fed back by each of the participants, calculating the intersection of the local registration algorithm instance list and all received registration algorithm instance lists, and taking the registration algorithm instance in the obtained intersection as the common algorithm instance;

[0010] Based on the preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants, a target algorithm instance is screened out from all the shared algorithm instances, wherein the preset weight is pre-configured weight information corresponding to each environmental parameter;

[0011] The target algorithm instance is used to perform data processing tasks on private data.

[0012] Optionally, the environmental parameter includes at least one of bandwidth size, data set size, length of data content and hardware performance index.

[0013] Optionally, when receiving the registration algorithm instance list fed back by each participant, calculating the intersection of the local registration algorithm instance list and all received registration algorithm instance lists includes:

[0014] Obtaining input parameters and output parameters of the data processing task;

[0015] Acquire, from the local registered algorithm instance list and all the received registered algorithm instance lists, an algorithm instance whose input parameters are the same as the input parameters of the data processing task and whose output parameters are the same as the output parameters of the data processing task as a valid algorithm instance;

[0016] Filter out valid algorithm instances contained in both the local registered algorithm instance list and the registered algorithm instance list fed back by each participant as the intersection of the local registered algorithm instance list and all received registered algorithm instance lists.

[0017] Optionally, the filtering out a target algorithm instance from all the shared algorithm instances based on a preset weight corresponding to each of the shared algorithm instances, the local environment parameters, and the environment parameters of each of the participants includes:

[0018] Calculate the efficiency score of each shared algorithm instance based on the preset weight corresponding to each shared algorithm instance, the local environment parameter and the environment parameter of each participant, and obtain the efficiency score corresponding to each shared algorithm instance;

[0019] The common algorithm instance corresponding to the efficiency score with the highest score is determined as the target algorithm instance.

[0020] Optionally, the calculating of the efficiency score of each shared algorithm instance based on a preset weight corresponding to each shared algorithm instance, the local environment parameter, and the environment parameter of each participant to obtain the efficiency score corresponding to each shared algorithm instance includes:

[0021] For each of the common algorithm instances, obtaining a preset efficiency evaluation model corresponding to the common algorithm instance;

[0022] For each of the shared algorithm instances, inputting the preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants into the preset efficiency evaluation model;

[0023] For each of the common algorithm instances, the preset efficiency evaluation model is used to perform efficiency calculation to obtain an efficiency score corresponding to the common algorithm instance.

[0024] In order to solve the above technical problems, the embodiment of the present application further provides a data processing device for private data, including:

[0025] The environment parameter acquisition module is used to obtain the local environment parameters and the environment parameters of each participant;

[0026] A registered algorithm instance acquisition module, used to acquire a local registered algorithm instance list and send an acquisition request for the registered algorithm instance list to each of the participants;

[0027] A shared algorithm instance determination module is used to calculate the intersection of the local registered algorithm instance list and all received registered algorithm instance lists when receiving the registered algorithm instance list fed back by each of the participants, and use the registered algorithm instance in the obtained intersection as the shared algorithm instance;

[0028] A target algorithm instance screening module, used to screen out a target algorithm instance from all the shared algorithm instances based on a preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants, wherein the preset weight is pre-configured weight information corresponding to each environmental parameter;

[0029] A data processing task execution module is used to use the target algorithm instance to perform data processing tasks on private data.

[0030] Optionally, the common algorithm instance determination module includes:

[0031] A task parameter acquisition unit, used to acquire input parameters and output parameters of the data processing task;

[0032] a valid algorithm instance determining unit, configured to obtain, from the local registered algorithm instance list and all the received registered algorithm instance lists, an algorithm instance whose input parameters are the same as the input parameters of the data processing task and whose output parameters are the same as the output parameters of the data processing task, as a valid algorithm instance;

[0033] The intersection determination unit is used to screen out valid algorithm instances contained in both the local registration algorithm instance list and the registration algorithm instance list fed back by each participant as the intersection of the local registration algorithm instance list and all received registration algorithm instance lists.

[0034] Optionally, the target algorithm instance screening module includes:

[0035] an efficiency score calculation unit, configured to calculate the efficiency score of each of the shared algorithm instances based on a preset weight corresponding to each of the shared algorithm instances, the local environment parameters, and the environment parameters of each of the participants, to obtain an efficiency score corresponding to each of the shared algorithm instances;

[0036] The target algorithm instance determining unit is used to determine the common algorithm instance corresponding to the efficiency score with the highest score as the target algorithm instance.

[0037] Optionally, the efficiency score calculation unit includes:

[0038] An acquisition subunit, configured to acquire, for each of the common algorithm instances, a preset efficiency evaluation model corresponding to the common algorithm instance;

[0039] An input subunit, for inputting, for each of the shared algorithm instances, the preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants into the preset efficiency evaluation model;

[0040] The calculation subunit is used to perform efficiency calculation for each of the common algorithm instances using the preset efficiency evaluation model to obtain an efficiency score corresponding to the common algorithm instance.

[0041] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data processing method for privacy data when executing the computer program.

[0042] In order to solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the data processing method for privacy data are implemented.

[0043] The data processing method, device, computer equipment and storage medium for privacy data provided by the embodiments of the present invention obtain a local registration algorithm instance list by obtaining local environmental parameters and the environmental parameters of each participant, and send a registration algorithm instance list acquisition request to each participant. When receiving the registration algorithm instance list fed back by each participant, the intersection of the local registration algorithm instance list and all received registration algorithm instance lists is calculated, and the registration algorithm instance in the obtained intersection is used as a common algorithm instance. Based on the preset weight corresponding to each common algorithm instance, the local environmental parameters and the environmental parameters of each participant, a target algorithm instance is screened out from all common algorithm instances, wherein the preset weight is the pre-configured weight information corresponding to each environmental parameter, and the target algorithm instance is used to perform the data processing task of privacy data, so as to realize automatic selection of the most efficient algorithm instance in the current environment for data processing, thereby improving the efficiency of the data processing task of privacy data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0045] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0046] Figure 2 is a flowchart of an embodiment of a method for processing private data of the present application;

[0047] Figure 3 is a schematic structural diagram of an embodiment of a data processing device for privacy data according to the present application;

[0048] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 ,like Figure 1 As shown, Figure 1 This is a schematic diagram of the application scenario of this scenario (to be supplemented later).

[0053] See also Figure 2 , Figure 2 A method for processing private data provided by an embodiment of the present invention is shown. The method is applied in Figure 1 The server in is used as an example to illustrate the details as follows:

[0054] S201: Obtain local environment parameters and environment parameters of each participant.

[0055] Optionally, the environmental parameters include at least one of bandwidth size, data set size, length of data content, and hardware performance indicators.

[0056] In this embodiment, considering that multi-party computing involves data transmission and calculation, it is necessary to consider the bandwidth size (affecting the transmission speed), data set size (affecting the transmission speed and calculation speed), data content length (affecting the calculation speed) and hardware performance indicators (affecting the calculation speed), etc., and adopt different calculation methods, that is, different algorithm instances, which require different data set sizes and data content lengths. For example, multi-party secure computing is performed on the initiator's data and the data of one participant. The initiator's bandwidth is 20M. Algorithm instance A is adopted, the data size to be transmitted is 300M, and the local calculation time is 20 seconds. Algorithm instance B is adopted, the data size to be transmitted is 100M, and the local calculation time is 30 seconds.

[0057] S202: Obtain a local registered algorithm instance list, and send a request to obtain the registered algorithm instance list to each participant.

[0058] Specifically, both the participating party and the initiator are equipped with an algorithm controller. The algorithm controller specifically refers to a collection of a series of algorithm instances holding the same type of algorithm. The algorithm controller provides an interface to the outside world for information exchange. The algorithm instances contained in each party's algorithm controller are not exactly the same.

[0059] S203: When receiving the registered algorithm instance list fed back by each participant, the intersection of the local registered algorithm instance list and all received registered algorithm instance lists is calculated, and the registered algorithm instance in the obtained intersection is used as the common algorithm instance.

[0060] Specifically, when receiving the registered algorithm instance list fed back by each participant, the initiator verifies and screens each algorithm instance in the received registered algorithm instance list, and obtains the available algorithm instances contained in each participant as the shared algorithm instances.

[0061] S204: Based on the preset weight corresponding to each shared algorithm instance, the local environmental parameters and the environmental parameters of each participant, the target algorithm instance is screened out from all shared algorithm instances, wherein the preset weight is the pre-configured weight information corresponding to each environmental parameter.

[0062] For the specific method of selecting the target algorithm instance, please refer to the description of the subsequent embodiments, and will not be described here to avoid repetition.

[0063] S205: Use the target algorithm instance to perform data processing tasks on private data.

[0064] In this embodiment, by obtaining local environmental parameters and the environmental parameters of each participant, a local registration algorithm instance list is obtained, and a request for obtaining the registration algorithm instance list is sent to each participant. When the registration algorithm instance list fed back by each participant is received, the intersection of the local registration algorithm instance list and all received registration algorithm instance lists is calculated, and the registration algorithm instance in the obtained intersection is used as a common algorithm instance. Based on the preset weight corresponding to each common algorithm instance, the local environmental parameters and the environmental parameters of each participant, a target algorithm instance is screened out from all common algorithm instances, wherein the preset weight is the pre-configured weight information corresponding to each environmental parameter. The target algorithm instance is used to perform data processing tasks for privacy data, so as to automatically select the most efficient algorithm instance in the current environment for data processing, thereby improving the efficiency of data processing tasks for privacy data.

[0065] In a specific optional implementation, in step S203, when receiving the registration algorithm instance list fed back by each participant, calculating the intersection of the local registration algorithm instance list and all received registration algorithm instance lists includes:

[0066] Get the input parameters and output parameters of the data processing task;

[0067] From the local registered algorithm instance list and all received registered algorithm instance lists, obtain an algorithm instance whose input parameters are the same as the input parameters of the data processing task and whose output parameters are the same as the output parameters of the data processing task as a valid algorithm instance;

[0068] Filter out the valid algorithm instances contained in both the local registered algorithm instance list and the registered algorithm instance list fed back by each participant as the intersection of the local registered algorithm instance list and all received registered algorithm instance lists.

[0069] In a specific optional implementation, in step S204, based on the preset weight corresponding to each shared algorithm instance, the local environment parameters and the environment parameters of each participant, the target algorithm instance is screened out from all shared algorithm instances, including:

[0070] Calculate the efficiency score of each shared algorithm instance based on the preset weight corresponding to each shared algorithm instance, the local environment parameters and the environment parameters of each participant, and obtain the efficiency score corresponding to each shared algorithm instance;

[0071] The common algorithm instance corresponding to the highest efficiency score is determined as the target algorithm instance.

[0072] The preset weight may be assigned to each environmental parameter based on the test results after multiple tests.

[0073] In a specific optional implementation, based on the preset weight corresponding to each shared algorithm instance, the local environment parameters and the environment parameters of each participant, the efficiency score of each shared algorithm instance is calculated, and the efficiency score corresponding to each shared algorithm instance is obtained, including:

[0074] For each common algorithm instance, obtaining a preset efficiency evaluation model corresponding to the common algorithm instance;

[0075] For each shared algorithm instance, the preset weight corresponding to each shared algorithm instance, the local environment parameters and the environment parameters of each participant are input into the preset efficiency evaluation model;

[0076] For each common algorithm instance, a preset efficiency evaluation model is used to calculate the efficiency and obtain the efficiency score corresponding to the common algorithm instance.

[0077] The preset efficiency evaluation model can be preset according to actual needs, and can be a formula or a machine learning model. For example, for each environmental parameter, a preset weight is assigned to the environmental parameter through the test results of multiple tests, and then the corresponding environmental parameters of the participants are weighted based on the preset weight, and each weighted environmental parameter is summed to obtain an efficiency score.

[0078] It should be noted that each registration algorithm instance corresponds to a preset efficiency evaluation model. The preset efficiency evaluation models corresponding to different registration algorithm instances may be the same or different, and are set according to actual application requirements. There is no restriction here.

[0079] In this embodiment, the efficiency of each shared algorithm instance in the current environment is calculated and evaluated through the preset efficiency evaluation model corresponding to the shared algorithm instance, so as to obtain the efficiency score of the shared algorithm instance in the current environment, which is conducive to the subsequent rapid and accurate selection of the target algorithm instance.

[0080] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0081] Figure 3 The principle block diagram of the data processing device for privacy data corresponding to the data processing method for privacy data in the above embodiment is shown. Figure 3 As shown, the data processing device for privacy data includes an environment parameter acquisition module 31, a registered algorithm instance acquisition module 32, a common algorithm instance determination module 33, a target algorithm instance screening module 34 and a data processing task execution module 35. The functional modules are described in detail as follows:

[0082] An environmental parameter acquisition module 31 is used to acquire local environmental parameters and environmental parameters of each participant;

[0083] A registered algorithm instance acquisition module 32 is used to acquire a local registered algorithm instance list and send an acquisition request for the registered algorithm instance list to each participant;

[0084] The shared algorithm instance determination module 33 is used to calculate the intersection of the local registered algorithm instance list and all received registered algorithm instance lists when receiving the registered algorithm instance list fed back by each participant, and use the registered algorithm instance in the obtained intersection as the shared algorithm instance;

[0085] The target algorithm instance screening module 34 is used to screen the target algorithm instance from all the shared algorithm instances based on the preset weight corresponding to each shared algorithm instance, the local environment parameters and the environment parameters of each participant, wherein the preset weight is the pre-configured weight information corresponding to each environment parameter;

[0086] The data processing task execution module 35 is used to perform data processing tasks on private data using a target algorithm instance.

[0087] Optionally, the common algorithm instance determination module 33 includes:

[0088] A task parameter acquisition unit, used to acquire input parameters and output parameters of a data processing task;

[0089] The valid algorithm instance determination unit is used to obtain, from the local registered algorithm instance list and all received registered algorithm instance lists, an algorithm instance whose input parameters are the same as the input parameters of the data processing task and whose output parameters are the same as the output parameters of the data processing task as a valid algorithm instance;

[0090] The intersection determination unit is used to screen out valid algorithm instances contained in both the local registered algorithm instance list and the registered algorithm instance list fed back by each participant as the intersection of the local registered algorithm instance list and all received registered algorithm instance lists.

[0091] Optionally, the target algorithm instance screening module 34 includes:

[0092] An efficiency score calculation unit, used to calculate the efficiency score of each shared algorithm instance based on a preset weight corresponding to each shared algorithm instance, a local environment parameter and an environment parameter of each participant, to obtain an efficiency score corresponding to each shared algorithm instance;

[0093] The target algorithm instance determination unit is used to determine the common algorithm instance corresponding to the highest efficiency score as the target algorithm instance.

[0094] Optionally, the efficiency score calculation unit includes:

[0095] An acquisition subunit, used for acquiring, for each common algorithm instance, a preset efficiency evaluation model corresponding to the common algorithm instance;

[0096] An input subunit, for inputting, for each shared algorithm instance, a preset weight corresponding to each shared algorithm instance, a local environmental parameter, and an environmental parameter of each participant into a preset efficiency evaluation model;

[0097] The calculation subunit is used to perform efficiency calculation for each common algorithm instance using a preset efficiency evaluation model to obtain an efficiency score corresponding to the common algorithm instance.

[0098] For the specific definition of the data processing device for privacy data, please refer to the definition of the data processing method for privacy data above, which will not be repeated here. Each module in the above-mentioned data processing device for privacy data can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0099] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0100] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components connected to the memory 41, the processor 42, and the network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0101] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0102] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D interface display memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as the program code for controlling electronic files, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0103] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code for controlling the electronic file.

[0104] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0105] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores an interface display program, and the interface display program can be executed by at least one processor so that the at least one processor performs the steps of the data processing method for privacy data as described above.

[0106] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0107] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for processing private data, characterized in that: The initiator server performs the following steps: Obtain local environment parameters and environment parameters of each participant; Obtain a local registered algorithm instance list, and send a request to obtain the registered algorithm instance list to each of the participants; When receiving the registration algorithm instance list fed back by each of the participants, calculating the intersection of the local registration algorithm instance list and all received registration algorithm instance lists, and taking the registration algorithm instance in the obtained intersection as the common algorithm instance; Based on the preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants, a target algorithm instance is screened out from all the shared algorithm instances, wherein the preset weight is pre-configured weight information corresponding to each environmental parameter; The target algorithm instance is used to perform data processing tasks on private data.

2. The method for processing private data according to claim 1, characterized in that: The environmental parameters include at least one of bandwidth size, data set size, length of data content, and hardware performance indicators.

3. The method for processing private data according to claim 1, characterized in that: When receiving the registration algorithm instance list fed back by each participant, calculating the intersection of the local registration algorithm instance list and all received registration algorithm instance lists includes: Obtaining input parameters and output parameters of the data processing task; Acquire, from the local registered algorithm instance list and all the received registered algorithm instance lists, an algorithm instance whose input parameters are the same as the input parameters of the data processing task and whose output parameters are the same as the output parameters of the data processing task as a valid algorithm instance; Filter out valid algorithm instances contained in both the local registered algorithm instance list and the registered algorithm instance list fed back by each participant as the intersection of the local registered algorithm instance list and all received registered algorithm instance lists.

4. The method for processing private data according to any one of claims 1 to 3, characterized in that: The filtering out a target algorithm instance from all the shared algorithm instances based on the preset weight corresponding to each of the shared algorithm instances, the local environment parameters, and the environment parameters of each of the participants includes: Calculate the efficiency score of each shared algorithm instance based on the preset weight corresponding to each shared algorithm instance, the local environment parameter and the environment parameter of each participant, and obtain the efficiency score corresponding to each shared algorithm instance; The common algorithm instance corresponding to the efficiency score with the highest score is determined as the target algorithm instance.

5. The method for processing private data according to claim 4, characterized in that: The calculating the efficiency score of each shared algorithm instance based on the preset weight corresponding to each shared algorithm instance, the local environment parameter and the environment parameter of each participant to obtain the efficiency score corresponding to each shared algorithm instance includes: For each of the common algorithm instances, obtaining a preset efficiency evaluation model corresponding to the common algorithm instance; For each of the shared algorithm instances, inputting the preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants into the preset efficiency evaluation model; For each of the common algorithm instances, the preset efficiency evaluation model is used to perform efficiency calculation to obtain an efficiency score corresponding to the common algorithm instance.

6. A data processing device for private data, characterized in that: The data processing device for the privacy data comprises: The environment parameter acquisition module is used to obtain the local environment parameters and the environment parameters of each participant; A registered algorithm instance acquisition module, used to acquire a local registered algorithm instance list and send an acquisition request for the registered algorithm instance list to each of the participants; A shared algorithm instance determination module is used to calculate the intersection of the local registered algorithm instance list and all received registered algorithm instance lists when receiving the registered algorithm instance list fed back by each of the participants, and use the registered algorithm instance in the obtained intersection as the shared algorithm instance; A target algorithm instance screening module, used to screen out a target algorithm instance from all the shared algorithm instances based on a preset weight corresponding to each of the shared algorithm instances, the local environmental parameters and the environmental parameters of each of the participants, wherein the preset weight is pre-configured weight information corresponding to each environmental parameter; A data processing task execution module is used to use the target algorithm instance to perform data processing tasks on private data.

7. The data processing device for private data according to claim 6, characterized in that: The common algorithm instance determination module comprises: A task parameter acquisition unit, used to acquire input parameters and output parameters of the data processing task; a valid algorithm instance determining unit, configured to obtain, from the local registered algorithm instance list and all the received registered algorithm instance lists, an algorithm instance whose input parameters are the same as the input parameters of the data processing task and whose output parameters are the same as the output parameters of the data processing task, as a valid algorithm instance; The intersection determination unit is used to screen out valid algorithm instances contained in both the local registration algorithm instance list and the registration algorithm instance list fed back by each participant as the intersection of the local registration algorithm instance list and all received registration algorithm instance lists.

8. The data processing device for private data according to claim 6, characterized in that: The target algorithm instance screening module includes: an efficiency score calculation unit, configured to calculate the efficiency score of each of the shared algorithm instances based on a preset weight corresponding to each of the shared algorithm instances, the local environment parameters, and the environment parameters of each of the participants, to obtain an efficiency score corresponding to each of the shared algorithm instances; The target algorithm instance determining unit is used to determine the common algorithm instance corresponding to the efficiency score with the highest score as the target algorithm instance.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for processing privacy data according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for processing privacy data according to any one of claims 1 to 5 is implemented.

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