A task processing method and device in secure multi-party computation

Abstract

The present application discloses a task processing method and apparatus in secure multi-party computation, including: determining triple data corresponding to a set of computing nodes; determining consumption data of a target task with respect to the triple data; allocating a corresponding set of computing nodes for the target task according to the triple data corresponding to the set of computing nodes and the consumption data of the target task; enabling the set of computing nodes to perform secure multi-party computation on the target task according to the triple data to determine a computation result corresponding to the target task; by combining the triple data and the consumption data for task allocation, the triple data of the set of computing nodes can satisfy the consumption of the target task, thereby achieving reasonable allocation of computing resources and improving the efficiency of secure multi-party computation.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a task processing method and apparatus in secure multi-party computation. Background Art

[0002] Secure Multi-Party Computation (MPC for short) is used to enable two or more users to collaboratively execute a certain computing task without revealing their respective private input information in a multi-user network environment where users do not trust each other. This technology is of great significance in scenarios of secret sharing and privacy protection.

[0003] Current mainstream secure multi-party computation can utilize a computing model based on garbled circuits. However, this computing model mainly targets the processing of two-party logical operations, is suitable for scenarios with low communication burdens, and has poor scalability. That is to say, the existing technology may not be able to meet the requirements in scenarios where multiple parties participate in the computation, the communication load is large, and the scalability requirements are high. Summary of the Invention

[0004] This application provides a task processing method and apparatus in secure multi-party computation to at least solve the above technical problems existing in the prior art.

[0005] In a first aspect, this application provides a task processing method in secure multi-party computation, including:

[0006] Determine the triple data corresponding to the computing node group;

[0007] Determine the consumption data of the target task for the triple data;

[0008] According to the triple data corresponding to the computing node group and the consumption data of the target task, allocate a corresponding computing node group for the target task; so that the computing node group performs secure multi-party computation on the target task according to the triple data to determine the computing result corresponding to the target task.

[0009] Preferably, it further includes:

[0010] Perform identity registration on multiple computing nodes and determine the performance information of each computing node;

[0011] Group the computing nodes according to the performance information of each computing node to establish the computing node group;

[0012] Alternatively, group the computing nodes according to a grouping instruction to establish the computing node group;

[0013] Alternatively, according to the number of the computing nodes, each of the computing nodes is grouped to establish the computing node groups.

[0014] Preferably, determining the triple data corresponding to the computing node group includes:

[0015] Determining the current triple data of the computing node group;

[0016] When the quantity of the current triple data is lower than a preset quantity threshold, the triple data is generated by using the computing node group.

[0017] Preferably, allocating a corresponding computing node group for the target task according to the triple data corresponding to the computing node group and the consumption data of the target task includes:

[0018] According to the target task, determining a corresponding target subtask; and determining the consumption data of the target subtask for the triple data;

[0019] According to the triple data corresponding to the computing node group and the consumption data of the target subtask, allocating a corresponding computing node group for the target subtask.

[0020] Preferably, it further includes:

[0021] Each of the computing nodes is grouped to establish an assisting node group.

[0022] Preferably, it further includes:

[0023] When the quantity of the triple data of the computing node group does not satisfy the consumption data of the target task, the triple data is generated by using the assisting node group and synchronized to the computing node group.

[0024] Preferably, the computing node group performs secure multi-party computation on the target task according to the triple data to determine the computation result corresponding to the target task, including:

[0025] Using the computing node group to obtain the task data corresponding to the target subtask from the data party;

[0026] According to the triple data, performing secure multi-party computation on the task data of the target subtask to determine a sub-computation result;

[0027] According to the sub-computation result, determining the computation result corresponding to the target task.

[0028] In a second aspect, the present application provides a task processing device in secure multi-party computation, including:

[0029] A triple data determination module, configured to determine triple data corresponding to a computing node group;

[0030] A consumption data determination module, configured to determine consumption data of a target task for the triple data;

[0031] A task scheduling module, configured to allocate a corresponding computing node group for the target task according to the triple data corresponding to the computing node group and the consumption data of the target task; so that the computing node group performs secure multi-party computation on the target task according to the triple data to determine a computation result corresponding to the target task.

[0032] In a third aspect, the present application provides a computer-readable storage medium storing a computer program for executing the task processing method in the secure multi-party computation of the present application.

[0033] In a fourth aspect, the present application provides an electronic device, including:

[0034] A processor;

[0035] A memory for storing executable instructions of the processor;

[0036] The processor is configured to read the executable instructions from the memory and execute the instructions to implement the task processing method in the secure multi-party computation of the present application.

[0037] Compared with the prior art, a task processing method and apparatus in secure multi-party computation provided by the present application allocate tasks by combining triple data and consumption data, so that the triple data of the computing node group can meet the consumption of the target task, thereby realizing reasonable allocation of computing resources and improving the efficiency of secure multi-party computation; by decomposing the target task and performing separate computations, it avoids the problem that the computing node group cannot meet its consumption when the target task is too complex, and at the same time meets the computing requirements in the case of multi-party participation in computation, large communication load, and high scalability requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flowchart of a task processing method in secure multi-party computation provided by an embodiment of the present application;

[0039] Figure 2 It is a schematic diagram of allocating a computing node group for a target subtask in a task processing method in secure multi-party computation provided by an embodiment of the present application;

[0040] Figure 3 It is a schematic diagram of the state change of a computing node group in a task processing method in secure multi-party computation provided by an embodiment of the present application;

[0041] Figure 4 It is a schematic flowchart of another task processing method in secure multi-party computation provided by an embodiment of the present application;

[0042] Figure 5 It is a schematic diagram of assisting node groups to synchronize triple data in another task processing method in secure multi-party computation provided by an embodiment of the present application;

[0043] Figure 6 It is a schematic flowchart of another task processing method in secure multi-party computation provided by an embodiment of the present application;

[0044] Figure 7 It is a schematic diagram of assisting node groups to synchronize triple data in another task processing method in secure multi-party computation provided by an embodiment of the present application;

[0045] Figure 8 It is a schematic structural diagram of a task processing device in secure multi-party computation provided by an embodiment of the present application. Detailed implementation manners

[0046] To make the objectives, features, and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0047] The current mainstream secure multi-party computation can utilize a computation model based on garbled circuits. However, this computation model mainly targets the processing of two-party logical operations, is suitable for scenarios with a relatively low communication burden, and has poor scalability. That is to say, the existing technologies may not be able to meet the requirements in scenarios where multiple parties participate in the computation, the communication load is large, and the scalability requirements are high.

[0048] Therefore, the embodiments of the present application will provide a task processing method in secure multi-party computation to at least solve the above technical problems existing in the prior art. As Figure 1 shown, the method in this embodiment includes the following steps:

[0049] Step 101: Determine the triple data corresponding to the computing node group.

[0050] Regarding the computing node group involved in this step, the following explanations are required. As Figure 2As shown, the secure multi-party computation involved in this application involves three parties: the scheduler, the computing party, and the data party. The task processing method described in this embodiment takes the scheduler as the execution entity, that is, through the scheduling and allocation of tasks by the scheduler, more efficient secure multi-party computation is achieved. Before that, the scheduler needs to overall plan and manage the computing resources involved in the computing party as a prerequisite for task allocation.

[0051] Specifically, the computing party includes multiple computing nodes. Before the implementation of the method in this embodiment, the identity registration of multiple computing nodes can be performed in advance, and only the registered computing nodes can participate in the subsequent process. Preferably, it can be anonymous identity registration. The advantage of registering the computing nodes is that it increases the security of the computing nodes and reduces the risk of the scheduler disclosing privacy in secure multi-party computation. Further, the performance information of each registered computing node can be determined, that is, the computing power of the computing node is evaluated to determine the scores of its main performance indicators such as CPU, memory, and bandwidth.

[0052] After registration and computing power evaluation, the computing nodes can be grouped. Each group of computing nodes after grouping can be called a computing node group. In this embodiment, no specific grouping method is limited, and any grouping method can be combined in the overall technical solution of this embodiment. The following are some specific grouping methods that can be provided exemplarily:

[0053] The computing nodes can be grouped according to the performance information of each computing node to establish a computing node group. That is, grouping based on performance makes the performance of each computing node group balanced. Or, the computing nodes can be grouped according to the number of computing nodes to establish a computing node group. That is, grouping based on the number makes the number of computing nodes in each computing node group meet the set requirements. Or, the computing nodes can be grouped according to the grouping instruction to establish a computing node group. That is, specifically specify which nodes belong to which computing node group artificially.

[0054] After establishing the computing node group, the triple data can be generated using the computing node group. As is well known in the art, the so-called triple data refers to a data resource that needs to be consumed during the secure multi-party computation process, which will not be elaborated in this embodiment. At the beginning of the establishment of the computing node group, a certain amount of triple data can be generated to facilitate the consumption of the triple data in the subsequent calculation process, so as to complete the processing of specific target tasks.

[0055] Before each calculation of the computing node group, that is, before executing a specific target task, the triple data corresponding to the computing node group can be determined, that is, the number of remaining triple data in the computing node group is determined, as described in this step. When the number of current triple data in the computing node group is lower than the preset quantity threshold, it means that the number of remaining triple data is too small to meet the consumption requirements in the next calculation. Therefore, the computing node group can be used to continue generating triple data as a supplement.

[0056] Combined with Figure 3 As shown, it reflects the state change process of the computing node group in the actual participation in secure multi-party. After establishing the computing node group, the computing node group can enter the initialization state (init state). Then it can enter the offline state (offline state) from the initialization state. In the offline state, the computing node group can generate triple data. If a computing node drops out in the offline state, the dropped computing node can enter the ungrouped state and then can be regrouped. When the number of triple data generated by the computing node group in the offline state reaches a certain amount, the computing node group can enter the preparation state (ready state) from the offline state. The computing node group in the preparation state can be assigned a specific target task and consume triple data. If a large number of computing nodes drop out in the preparation state (for example, more than 1 / 3 drop out), the computing node group needs to transition to the fault state (fault state). If the computing node group in the preparation state is assigned a target task and starts to operate, it enters the online state (normal online state). After the calculation is completed, it will return to the preparation state (ready state). If a large number of computing nodes drop out during the calculation (for example, more than 1 / 3 drop out), the computing node group will also transition to the fault state (fault state).

[0057] Step 102: Determine the consumption data of the target task for the triple data.

[0058] The target task is the task that actually needs to be executed in secure multi-party computing. Before calculating the target task, it needs to be evaluated, that is, to determine its consumption data for the triple data. The consumption data represents the number of triple data required to complete the target task.

[0059] It is known that the prior art is mainly applicable to scenarios with low communication burden and has poor scalability. That is to say, in complex tasks, due to the situation of multi-party participation in computing, large communication load, and high scalability requirements, the prior art cannot meet the needs. In this embodiment, when the target task is a complex task, the target task can be decomposed, that is, the corresponding target subtasks are determined according to the target task; and the consumption data of the target subtasks for the triple data is determined. The consumption data of the target subtasks for the triple data represents the quantity of triple data required to complete the target subtasks.

[0060] Step 103: Allocate a corresponding computing node group for the target task according to the triple data corresponding to the computing node group and the consumption data of the target task.

[0061] In this embodiment, the allocation of the target task will be realized according to the triple data corresponding to the computing node group and the consumption data of the target task. That is, the target task is allocated to the computing node group that can meet its consumption for computing. Specifically, the triple data and the consumption data can be compared. When the triple data of a certain computing node group is less than the consumption data, it means that the computing node group cannot meet the consumption of the target task, and the target task should not be allocated to this computing node group. On the contrary, when the triple data of a certain computing node group is greater than the consumption data, it means that the computing node group can meet the consumption of the target task, and the target task can be allocated to this computing node group. Or, when the triple data of a certain computing node group is much greater than the consumption data, it means that although the computing node group can meet the consumption of the target task, waste of consumed resources (triple data) may occur if the target task is allocated to this computing node group. Especially in the case of multiple target tasks, overall trade-offs should be made during the allocation process, so that each target task is allocated to the computing node group that can meet the consumption for operation, realizing the balance of consumed resources.

[0062] In the case where the target task is decomposed into multiple target subtasks, a corresponding computing node group can be allocated for the target subtasks according to the triple data corresponding to the computing node group and the consumption data of the target subtasks. That is to say, if the target task is too complex and the quantity of triple data it consumes is too large, it may be that the computing node group cannot meet its consumption. In this case, decomposing it into multiple target subtasks and performing calculations separately can make each computing node group meet the consumption of the target subtasks, thus enabling the calculation to be completed smoothly. In this embodiment, through the above method, the computing requirements in the case of multi-party participation in computing, large communication load, and high scalability requirements are met, and the efficiency of secure multi-party computing is improved.

[0063] After the task assignment is completed, the computing node group of the computing party will perform secure multi-party computation on the target task based on the triple data to determine the computation result corresponding to the target task.

[0064] As Figure 2 shown, during the process of secure multi-party computation, after the computing node group assigns the target subtasks, the scheduler can send the grouping information of the computing nodes to the data party. On the other hand, the computing node group will obtain the task data corresponding to the target subtasks from the data party. The task data corresponding to the target subtasks is the data actually involved in the target subtasks. Then, the computing node group performs secure multi-party computation on the task data of the target subtasks based on the triple data to determine the sub-computation result; based on the sub-computation result, the computation result corresponding to the target task is determined. That is, during the actual process of secure multi-party computation, the computing node group obtains the task data from the data party and consumes the triple data to perform operations on the task data, thereby obtaining the final computation result. Thus, the secure multi-party computation for the target task is completed in this embodiment.

[0065] As can be seen from the above technical solutions, the beneficial effects of this embodiment are as follows: By combining the triple data and consuming data for task assignment, the triple data of the computing node group can meet the consumption of the target task, thereby realizing the reasonable allocation of computing resources and improving the efficiency of secure multi-party computation; by decomposing and separately computing the target task, the problem that the computing node group cannot meet its consumption when the target task is too complex is avoided, and at the same time, the computing requirements in the case of multi-party participation in computation, large communication load, and high scalability requirements are met.

[0066] Figure 1 The shown is only the basic embodiment of the method described in this application. Based on it, with certain optimizations and expansions, other preferred embodiments of the method can also be obtained.

[0067] As Figure 4 shown, it is another specific embodiment of the task processing method in the secure multi-party computation described in this application. This embodiment is further expanded based on the foregoing embodiment. The method specifically includes the following steps:

[0068] Step 401, determine the triple data corresponding to the computing node group.

[0069] Step 402, determine the consumption data of the target task for the triple data.

[0070] Step 403, based on the triple data corresponding to the computing node group and the consumption data of the target task, assign the corresponding computing node group to the target task.

[0071] Step 404, group each computing node to establish an assisting node group.

[0072] In this embodiment, some additional computing nodes are selected and grouped to obtain an assisting node group. The computing nodes in the assisting node group are all computing nodes that have undergone anonymous registration. The specific grouping method can also refer to Figure 1 the description in the illustrated embodiment. In fact, it can also be considered that there is no essential difference between the computing node group and the assisting node group.

[0073] The role of the assisting node group is to generate triple data. Combining Figure 3 as shown, since any node group in the offline state can generate triple data, it can be considered that any node group in this state can logically serve as an assisting node group. The assisting node group usually remains in the offline state, that is, it does not directly participate in secure multi-party computing. Each assisting node group will generate a certain amount of triple data for reserve during the computing process.

[0074] Step 405: Use the computing node group to perform secure multi-party computing on the target task according to the triple data to determine the computing result corresponding to the target task.

[0075] Step 406: When the quantity of the triple data of the computing node group does not meet the consumption data of the target task, use the assisting node group to generate triple data and synchronize it to the computing node group.

[0076] During the actual process of secure multi-party computing, sometimes due to the excessive complexity and large consumption of the target task, the triple data of each computing node group cannot meet the consumption. At this time, the reserve of triple data in the assisting node group can be used to solve this problem, refer to Figure 5 as shown.

[0077] As previously known, the assisting node group in the offline state can generate triple data as a reserve during the computing process. Therefore, when the computing node group cannot meet the consumption, or when the triple data of the computing node group itself is about to be consumed, the reserved triple data in the assisting node group can be synchronized to the computing triples as a supplement.

[0078] After the triple data of the computing node group is supplemented, the triple data can continue to be consumed for computing. Figure 5 In the situation shown in, the target task is assigned to the computing node group. There is an assisting node group to supplement the triple data for this computing node group. Of course, in other cases, the number of computing node groups and assisting node groups is not limited. The assisting node group can supplement the computing node group one-to-one, or can supplement it in a many-to-one manner.

[0079] Through the above method of supplementing triple data using the assisting node group, in this embodiment, the problem that the triple data of the computing node group cannot meet the consumption when the target task is too complex and the consumption of triple data is too large is solved.

[0080] As Figure 6 shown, it is another specific embodiment of the task processing method in the secure multi-party computing described in this application. On the basis of the foregoing embodiment, this embodiment is further expanded. The method specifically includes the following steps:

[0081] Step 601: Group each computing node to establish a computing node group and an assisting node group.

[0082] Step 602: Determine the triple data corresponding to the computing node group.

[0083] Step 603: Use the assisting node group to generate triple data.

[0084] Step 604: According to the target task, determine the corresponding target subtask; and determine the consumption data of the target subtask for the triple data.

[0085] Step 605: According to the triple data corresponding to the computing node group and the consumption data of the target subtask, allocate the corresponding computing node group to the target subtask.

[0086] Step 606: Use the computing node group to perform secure multi-party computing on the task data of the target subtask to determine the sub-computation result.

[0087] Step 607: When the quantity of the triple data of the computing node group does not meet the consumption data of the target subtask, synchronize the triple data generated by the assisting node group to the computing node group.

[0088] Step 608: According to the sub-computation result, determine the computation result corresponding to the target task.

[0089] In Figure 1 the shown embodiment, the complex target task can be decomposed and processed, and the decomposed target subtasks are allocated to the computing node group for execution to improve the computing efficiency. This processing method can be called the decomposition mode. In Figure 4 the shown embodiment, the assisting node group can be used to supplement the triple data of the computing node group to meet a larger consumption of triple data. This processing method can be called the assisting mode.

[0090] In this embodiment, the decomposition mode and the assisting mode can be combined and used, such as Figure 7As shown. That is, when the target subtask still consumes too much, so that the triple data of the computing node group cannot meet the consumption. At this time, the reserve of triple data in the assisting node group can be used to solve this problem. In this embodiment, the target task is decomposed into subtask 1 and subtask 2. Subtask 1 is assigned to computing node group A for processing, and subtask 2 is assigned to computing node group B for processing.

[0091] Since both subtask 1 and subtask 2 need to consume a large amount of triple data, the triple data of computing node group A and computing node group B themselves cannot meet the consumption. Therefore, in Figure 7 the example shown, assisting node group a synchronizes triple data to computing node group A to meet the consumption of subtask 1. Assisting node group b synchronizes triple data to computing node group B to meet the consumption of subtask 2. Thus, the consumption of each target subtask can be met, enabling the target subtask to complete the calculation and obtain the calculation result.

[0092] As Figure 8 shown, it is a specific embodiment of the task processing device in the secure multi-party computing described in this application. The device in this embodiment is an entity device for executing Figures 1 to 7 the method described above. Its technical solution is essentially the same as that of the above embodiment, and the corresponding descriptions in the above embodiment also apply to this embodiment. The device in this embodiment includes:

[0093] Triple data determination module 801, used to determine the triple data corresponding to the computing node group.

[0094] Consumption data determination module 802, used to determine the consumption data of the target task for the triple data.

[0095] Task scheduling module 803, used to allocate a corresponding computing node group for the target task according to the triple data corresponding to the computing node group and the consumption data of the target task; so that the computing node group performs secure multi-party calculation on the target task according to the triple data to determine the calculation result corresponding to the target task.

[0096] In addition, on the basis of the embodiment shown in Figure 8 preferably, it further includes:

[0097] It further includes:

[0098] Computing node group establishment module 804, used to establish a computing node group.

[0099] The computing node group establishment module 804 includes:

[0100] Registration unit 841, used to perform identity registration on multiple computing nodes and determine the performance information of each computing node.

[0101] A grouping unit 842, configured to group each computing node according to the performance information of each computing node to establish a computing node group; or group each computing node according to a grouping instruction to establish a computing node group; or group each computing node according to the number of computing nodes to establish a computing node group.

[0102] The triple data determination module 801 includes:

[0103] A triple data quantity determination unit 811, configured to determine the current triple data of the computing node group.

[0104] A triple data generation unit 812, configured to generate triple data by using the computing node group when the quantity of the current triple data is lower than a preset quantity threshold.

[0105] The task scheduling module 803 includes:

[0106] A task decomposition unit 831, configured to determine a corresponding target subtask according to a target task.

[0107] A task allocation unit 832, configured to allocate a corresponding computing node group to the target subtask according to the triple data corresponding to the computing node group and the consumption data of the target subtask.

[0108] It further includes:

[0109] An assisting node group establishment module 805, configured to group each computing node to establish an assisting node group. When the quantity of the triple data of the computing node group does not meet the consumption data of the target task, generate triple data by using the assisting node group and synchronize it to the computing node group.

[0110] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions that, when run by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0111] The computer program product may be written in any combination of one or more programming languages for programming code to execute the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code may be executed entirely on a user computing device, partially on a user device, executed as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0112] In addition, an embodiment of the present application may also be a computer-readable storage medium storing computer program instructions, which, when run by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present application described in the above "Exemplary Methods" section of this specification.

[0113] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0114] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for illustrative purposes and for ease of understanding, rather than limitations. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0115] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended words, meaning "including but not limited to", and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or", and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with each other.

[0116] It should also be noted that in the devices, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0117] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Thus, the present application is not intended to be limited to the aspects shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0118] The above description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although several example aspects and embodiments have been discussed above, those skilled in the art will recognize some of their variations, modifications, alterations, additions, and subcombinations.

Claims

1. A task processing method in secure multi-party computation, comprising: Determining triple data corresponding to a computing node group; Determining consumption data of a target task for the triple data; According to the triple data corresponding to the computing node group and the consumption data of the target task, allocating a corresponding computing node group for the target task; so that the computing node group performs secure multi-party computation on the target task according to the triple data to determine a computing result corresponding to the target task; The determining the triple data corresponding to the computing node group includes: Determining current triple data of the computing node group; When the quantity of the current triple data is lower than a preset quantity threshold, generating the triple data by using the computing node group.

2. The method according to claim 1, further comprising: Performing identity registration on multiple computing nodes and determining performance information of each computing node; Grouping each computing node according to the performance information of each computing node to establish the computing node group; Alternatively, grouping each computing node according to a grouping instruction to establish the computing node group; Alternatively, grouping each computing node according to the quantity of the computing nodes to establish the computing node group.

3. The method according to claim 1, wherein the allocating a corresponding computing node group for the target task according to the triple data corresponding to the computing node group and the consumption data of the target task includes: Determining a corresponding target subtask according to the target task; And determining consumption data of the target subtask for the triple data; According to the triple data corresponding to the computing node group and the consumption data of the target subtask, allocating a corresponding computing node group for the target subtask.

4. The method according to claim 2, further comprising: Grouping each computing node to establish an assisting node group.

5. The method according to claim 4, further comprising: When the quantity of the triple data of the computing node group does not satisfy the consumption data of the target task, generating the triple data by using the assisting node group and synchronizing the triple data to the computing node group.

6. The method according to claim 3, wherein the computing node group performs secure multi-party computation on the target task according to the triple data to determine a computing result corresponding to the target task includes: Using the computing node group to obtain task data corresponding to the target subtask from a data party; Performing secure multi-party computation on the task data of the target subtask according to the triple data to determine a sub-computing result; Determining a computing result corresponding to the target task according to the sub-computing result.

7. A task processing device in secure multi-party computation, comprising: A triple data determining module, configured to determine triple data corresponding to a computing node group; A consumption data determining module, configured to determine consumption data of a target task for the triple data; A task scheduling module, configured to allocate a corresponding computing node group for the target task according to the triple data corresponding to the computing node group and the consumption data of the target task, so that the computing node group performs secure multi-party computation on the target task according to the triple data to determine the computation result corresponding to the target task; A triple data determination module, further configured to determine the current triple data of the computing node group; When the quantity of the current triple data is lower than a preset quantity threshold, generate the triple data by using the computing node group.

8. A computer-readable storage medium, storing a computer program for executing the task processing method in the secure multi-party computation according to any one of claims 1-6 above.

9. An electronic device, comprising: A processor; A memory for storing executable instructions of the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the task processing method in the secure multi-party computation according to any one of claims 1-6 above.

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