A privacy computing task scheduling method and system based on blockchain

Abstract

This application provides a blockchain-based privacy-preserving computing task scheduling method and system. The method extracts the execution status of privacy-preserving computing tasks in the blockchain from task progress information, and then determines the elastic computing power of the blockchain's scheduling resources for privacy protection based on the execution status and the task offloading strategy used in the blockchain's computing resource scheduling. The computing resources in the blockchain are divided into multiple computing resource nodes, and the resource sharing relationships between each computing resource node are determined. Based on the resource sharing relationships, the privacy protection cost of each computing resource node for the privacy-preserving computing task is determined. The conflict between computing power allocation and privacy protection in the blockchain is evaluated using the privacy protection costs and elastic computing power to obtain a balance value for scheduling conflicts in the blockchain. Based on this balance value, privacy-preserving computing tasks are evenly distributed across the computing resource nodes in the blockchain. This scheme can achieve balanced distribution of privacy-preserving computing tasks in the blockchain.

Description

Technical Field

[0001] The present application relates to the field of task scheduling technology. More specifically, the present application relates to a privacy computing task scheduling method and system based on blockchain. Background Art

[0002] The core characteristics of blockchain include decentralization, data immutability, anonymity, transparency, and support for smart contracts. These characteristics give blockchain significant advantages in solving trust issues and security risks in traditional centralized systems, and can also realize decentralized applications.

[0003] Privacy computing tasks involve the processing of sensitive data, and usually include encrypting, calculating, and analyzing the private information of individuals, enterprises, or organizations. In a decentralized blockchain network, task scheduling needs to consider how to effectively distribute computing tasks among multiple nodes without exposing or leaking sensitive data. Traditional task scheduling methods will lead to uneven task distribution, resulting in some block nodes carrying too many sensitive data processing tasks. This will not only lead to unbalanced computing loads, but also increase the risk of data leakage. For example, if a block node processes a large amount of private data for a long time, it may become a target of attack, or suffer a man-in-the-middle attack during data transmission, thereby exposing the user's private information. Therefore, how to achieve a balanced distribution of privacy computing tasks in the blockchain has become a difficult problem facing the industry. Summary of the Invention

[0004] This application provides a blockchain-based privacy computing task scheduling method and system, which can achieve balanced distribution of privacy computing tasks in the blockchain.

[0005] In a first aspect, the present application provides a method for scheduling privacy-preserving computing tasks based on blockchain, comprising:

[0006] When executing privacy computing tasks on the blockchain, real-time monitoring of the task progress information of privacy computing tasks on the blockchain;

[0007] Extracting the execution status of the privacy-preserving computing task in the blockchain from the task progress information, and then determining the elastic computing power of the scheduling resources in the blockchain for privacy protection based on the execution status and the task offloading strategy for the scheduling of computing resources in the blockchain;

[0008] Use a hierarchical structure to divide the computing power resources in the blockchain into multiple computing power resource nodes, and then determine the resource sharing relationship between each computing power resource node. Based on the resource sharing relationship and the privacy protection characteristics of the blockchain, determine the privacy protection cost of each computing power resource node for the privacy computing task;

[0009] The conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated by using various privacy protection costs and the elastic computing power to obtain a balance value of scheduling conflicts in the blockchain. Based on the balance value of scheduling conflicts, privacy computing tasks are evenly distributed among the computing power resource nodes in the blockchain.

[0010] In some embodiments, extracting the execution status of the privacy computing task in the blockchain from the task progress information specifically includes:

[0011] Get all the computing tasks being executed in the blockchain;

[0012] For each computing task, extracting the task completion progress and task completion speed of the computing task from the task progress information;

[0013] Determining the execution characteristics of the computing tasks by the task completion progress and the task completion speed, thereby obtaining the execution characteristics of each computing task;

[0014] Extract the execution status of privacy computing tasks in the blockchain from all execution features.

[0015] In some embodiments, determining the elastic computing power of the scheduling resources for privacy protection in the blockchain based on the execution state and the task offloading strategy for the scheduling of computing resources in the blockchain specifically includes:

[0016] Obtain task offloading strategies for computing resource scheduling in blockchain;

[0017] For each computing task, extracting an execution state value of the computing task from the execution state;

[0018] Performing an scalability evaluation on the execution state value using the task offloading strategy to obtain a scalable value of the computing power in the computing task, and then obtaining a scalable value of the computing power in each computing task;

[0019] Determine the elastic computing power of scheduling resources for privacy protection in the blockchain based on all scalable values.

[0020] In some embodiments, using a hierarchical structure to divide the computing resources in a blockchain into multiple computing resource nodes specifically includes:

[0021] Obtain all resource blocks of the blockchain and determine the computing power characteristics of each resource block;

[0022] All computing power features are classified into different levels through a hierarchical structure to obtain multiple computing power levels.

[0023] All resource blocks are divided into multiple computing resource nodes according to various computing power levels.

[0024] In some embodiments, determining the resource sharing relationship between computing resource nodes specifically includes:

[0025] For each computing resource node, obtain all shared blocks between the computing resource node and other computing resource nodes;

[0026] Determine the topological relationship graph of computing power resource nodes through all shared blocks, and then obtain the topological relationship graph of each computing power resource node;

[0027] Generate resource sharing relationships between various computing resource nodes based on all topological association graphs.

[0028] In some embodiments, determining the privacy protection cost of each computing resource node for a privacy-preserving computing task based on the resource sharing relationship and the privacy protection characteristics in the blockchain specifically includes:

[0029] For each computing resource node, extract the resource sharing characteristics of the computing resource node from the resource sharing relationship;

[0030] Extract the trust of computing resource nodes in privacy computing tasks from the privacy protection features in the blockchain;

[0031] By associating the trust degree through the resource sharing feature, the privacy protection cost of the computing power resource node for the privacy computing task is obtained, and then the privacy protection cost of each computing power resource node for the privacy computing task is obtained.

[0032] In some embodiments, the privacy computing task is a distributed computing task based on homomorphic privacy encryption.

[0033] In a second aspect, this application provides a blockchain-based privacy computing task scheduling system, including:

[0034] A monitoring module is used to monitor the progress of privacy-preserving computing tasks in real time when the blockchain is executing them.

[0035] A processing module, configured to extract the execution status of the privacy-preserving computing task in the blockchain from the task progress information, and then determine the elastic computing power of the scheduling resources in the blockchain for privacy protection based on the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain;

[0036] The processing module is further configured to divide the computing resources in the blockchain into a plurality of computing resource nodes using a hierarchical structure, thereby determining a resource sharing relationship between the computing resource nodes, and determining a privacy protection cost of each computing resource node for a privacy-preserving computing task based on the resource sharing relationship and the privacy protection characteristics of the blockchain;

[0037] An execution module is used to evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain using various privacy protection costs and the elastic computing power, obtain a balance value of scheduling conflicts in the blockchain, and evenly distribute privacy computing tasks to various computing power resource nodes in the blockchain based on the balance value of scheduling conflicts.

[0038] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned blockchain-based privacy computing task scheduling method.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned blockchain-based privacy computing task scheduling method.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] The present application provides a blockchain-based privacy computing task scheduling method and system. When the blockchain executes the privacy computing task, the task progress information of the privacy computing task in the blockchain is monitored in real time; the execution status of the privacy computing task in the blockchain is extracted from the task progress information, and then the elastic computing power of the scheduling resources for privacy protection in the blockchain is determined through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain; the computing power resources in the blockchain are divided into multiple computing power resource nodes using a hierarchical structure, and then the resource sharing relationship between each computing power resource node is determined, and the privacy protection cost of each computing power resource node for the privacy computing task is determined according to the resource sharing relationship and the privacy protection characteristics in the blockchain; the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated through each privacy protection cost and the elastic computing power to obtain the balance value of the scheduling conflict in the blockchain, and the privacy computing tasks are evenly distributed to each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

[0042] It can be seen that in this application, the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated by various privacy protection costs and the elastic computing power, and the balance value of the scheduling conflict in the blockchain is obtained. Based on the balance value of the scheduling conflict, the privacy computing tasks are evenly distributed to each computing power resource node in the blockchain; first, the elastic computing power is determined to obtain the ability of the computing power resources in the blockchain to be flexibly adjusted in task scheduling. In privacy computing tasks, the complexity and data sensitivity of the tasks may vary with time and computing stages. The dynamic adjustment of the elastic computing power ensures the efficient utilization of computing power resources. When the computing power of the computing power resource node is insufficient, the elastic computing power can be reallocated in the network to ensure the smooth execution of the task and avoid performance bottlenecks. At the same time, the determination of the elastic computing power can also help prioritize the allocation of resources for tasks with high privacy protection requirements. Source, reducing the risk of privacy leakage caused by uneven distribution of computing power, thereby achieving a balance between computing performance and privacy protection, and effectively promoting the balanced distribution of tasks; then, determining the privacy protection cost representation can obtain the cost required for computing power resource nodes to support privacy computing tasks under the premise of meeting privacy protection requirements, which helps to identify the adaptability of each node to privacy tasks, thereby avoiding nodes with low privacy protection capabilities from taking on highly sensitive tasks, reducing the risk of privacy leakage. At the same time, in the task allocation process, by weighing the privacy protection cost and computing power requirements, tasks can be effectively allocated to nodes with matching privacy protection capabilities and computing power capabilities, thereby maximizing the use of network resources and achieving overall load balancing, thereby improving resource utilization efficiency and privacy protection level; in summary, based on the above scheme, balanced distribution of privacy computing tasks in the blockchain can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 This is an exemplary flow chart of a method for scheduling privacy-preserving computing tasks based on blockchain according to some embodiments of the present application;

[0045] Figure 2 This is a control logic diagram of the blockchain shown in some embodiments of the present application;

[0046] Figure 3 is a schematic diagram of a process for determining a balance value of a scheduling conflict according to some embodiments of the present application;

[0047] Figure 4This is a schematic diagram of the structure of a privacy-preserving computing task scheduling system based on blockchain according to some embodiments of the present application;

[0048] Figure 5 This is a structural diagram of a computer device that implements a blockchain-based privacy computing task scheduling method according to some embodiments of the present application. DETAILED DESCRIPTION

[0049] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0050] refer to Figure 1 This figure is an exemplary flow chart of a blockchain-based privacy-preserving computing task scheduling method according to some embodiments of the present application. The blockchain-based privacy-preserving computing task scheduling method mainly includes the following steps:

[0051] In step 101, when the blockchain executes the privacy computing task, the task progress information of the privacy computing task in the blockchain is monitored in real time.

[0052] It should be noted that in this application, task progress information uses information describing the completion status of privacy computing tasks in the blockchain; the privacy computing tasks are distributed computing tasks based on homomorphic privacy encryption; in specific implementation, when executing privacy computing tasks on the blockchain, all computing tasks being executed in the blockchain are obtained, and the task completion progress and task completion speed of each computing task are monitored in real time. The task completion progress is the percentage of completed tasks, and the task completion speed is the progress of completing tasks per second. The collection of task completion progress and task completion speed of all computing tasks can be used as the task progress information of the privacy computing task in the blockchain.

[0053] In some embodiments, reference Figure 2 As described, the figure is a control logic diagram of the blockchain shown in some embodiments of the present application. The figure shows the control logic structure of a blockchain network, which includes a control center and multiple node groups; the control center is responsible for managing the main chain and sub-chain of the entire network, and communicates and synchronizes data with each node group through the main chain. Each node group is composed of multiple nodes, such as node group A and node group N. Each node group is connected internally through a sub-chain, so that data sharing and communication between nodes in the group can be realized.

[0054] In this control logic structure, node groups A and N each contain four nodes. Each node is connected to other nodes within the group via a secondary chain and to the control center via the main chain. The control center sends instructions and data to each node group through the main chain and receives information from each node group. This ensures the decentralized nature of the blockchain network while enabling effective management and coordination of the entire network through the control center. This structure enables efficient data transmission and processing, while also ensuring the security and reliability of the blockchain network.

[0055] In step 102, the execution status of the privacy computing task in the blockchain is extracted from the task progress information, and then the elastic computing power of the scheduling resources in the blockchain for privacy protection is determined based on the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain.

[0056] In some embodiments, extracting the execution status of a privacy-preserving computing task in a blockchain from the task progress information can be achieved by using the following steps:

[0057] Get all the computing tasks being executed in the blockchain;

[0058] For each computing task, extracting the task completion progress and task completion speed of the computing task from the task progress information;

[0059] Determining the execution characteristics of the computing tasks by the task completion progress and the task completion speed, thereby obtaining the execution characteristics of each computing task;

[0060] Extract the execution status of privacy computing tasks in the blockchain from all execution features.

[0061] It should be noted that in this application, the execution status represents the real-time execution progress information of the computing task on the computing resource node, including the status of the task being started, in progress, suspended, completed, etc.; in specific implementation, first, all computing tasks being executed in the blockchain are obtained; secondly, for each computing task, the task completion progress and task completion speed of the computing task are extracted from the task progress information; then, the execution characteristics of the computing task are determined by the task completion progress and the task completion speed, and the execution characteristics of each computing task are obtained. This can be achieved in the following way: that is, the result of (1-task completion progress) / task completion speed can be used as the execution characteristic of the computing task. The execution characteristics of each computing task can be obtained by the above method, which represents the time required for the unfinished part of the computing task; finally, a clustering algorithm can be used to classify all execution characteristics into multiple state clusters. For each state cluster, the mean of all execution characteristics in the state cluster can be used as the execution state value of the state cluster. The execution state value of each state cluster can be obtained by the above method, so that the set of all execution state values ​​can be used as the execution state of the privacy computing task in the blockchain.

[0062] In some embodiments, determining the elastic computing power of the scheduling resources in the blockchain for privacy protection based on the execution state and the task offloading strategy of the computing power resource scheduling in the blockchain can be achieved by the following steps:

[0063] Obtain task offloading strategies for computing resource scheduling in blockchain;

[0064] For each computing task, extracting an execution state value of the computing task from the execution state;

[0065] Performing an scalability evaluation on the execution state value using the task offloading strategy to obtain a scalable value of the computing power in the computing task, and then obtaining a scalable value of the computing power in each computing task;

[0066] Determine the elastic computing power of scheduling resources for privacy protection in the blockchain based on all scalable values.

[0067] In the specific implementation, first, the task offloading strategy for computing resource scheduling in the blockchain can be obtained in the following manner, namely: the task offloading strategy for computing resource scheduling in the blockchain can be obtained from the control center of the blockchain; secondly, for each computing task, the execution state value of the computing task can be extracted from the execution state and can be implemented in the following manner, namely: for each computing task, the execution state value of the state cluster to which the computing task belongs in the execution state can be used as the execution state value of the computing task; then, the scalability evaluation of the execution state value is performed on the execution state value through the task offloading strategy to obtain the scalable value of the computing power in the computing task, and then the scalability of each computing task is obtained. The scalable value of computing power in a computing task can be achieved in the following way: initialize a computing power evaluation model based on a neural network, use the task offloading strategy as the computing power release strategy of the computing task in the computing power evaluation model, use the execution status value as the output label in the computing power evaluation model, and use the computing power evaluation model to evaluate the scalable computing power in the computing task, so that the evaluation result of the computing power evaluation model can be used as the scalable value of the computing power in the computing task. The scalable value of the computing power in each computing task can be obtained in the above way; finally, the set of all scalable values ​​can be used as the elastic computing power for scheduling resources in the blockchain for privacy protection.

[0068] It should be noted that in this application, elastic computing power refers to the computing power of the computing power resource node that can schedule tasks without reducing performance when processing computing tasks; the task offloading strategy refers to the decision rule for removing computing tasks from the computing power resource node in the blockchain; the execution status value represents the current execution progress of the privacy computing task on the computing power resource node; the scalability value represents the scalability measurement of the computing power resource node when processing computing tasks; the computing power evaluation model is a neural network-based model that aims to evaluate the scalability of computing tasks on different computing power resource nodes. In the computing power evaluation model, the task offloading strategy is used as the input feature of the model, representing how to allocate computing tasks to different nodes according to task requirements and node capabilities, and the execution status value is used as the output label to reflect the actual execution progress of the task. By training the computing power evaluation model, the relationship between the task offloading strategy and the task execution characteristics can be learned, thereby predicting the computing power scalability of different computing tasks on different nodes, and finally outputting the scalability value to guide the reasonable scheduling and resource allocation of tasks.

[0069] In step 103, a hierarchical structure is used to divide the computing resources in the blockchain into multiple computing resource nodes, and then the resource sharing relationship between each computing resource node is determined. Based on the resource sharing relationship and the privacy protection characteristics in the blockchain, the privacy protection cost of each computing resource node for the privacy computing task is determined.

[0070] In some embodiments, using a hierarchical structure to divide the computing resources in a blockchain into multiple computing resource nodes can be achieved by using the following steps:

[0071] Obtain all resource blocks of the blockchain and determine the computing power characteristics of each resource block;

[0072] All computing power features are classified into different levels through a hierarchical structure to obtain multiple computing power levels.

[0073] All resource blocks are divided into multiple computing resource nodes according to various computing power levels.

[0074] In the specific implementation, first, all resource blocks of the blockchain are obtained, and then the computing power characteristics of each resource block are determined. This can be achieved in the following way: all resource blocks and the computing power range of each resource block can be obtained from the control center of the blockchain. Each computing power range can be used as the computing power characteristic of the corresponding resource block, and the computing power characteristics of each resource block can be obtained; then, all computing power characteristics are graded by a hierarchical structure to obtain multiple computing power levels. This can be achieved in the following way: a hierarchical structure is obtained from the control center of the blockchain, and the hierarchical structure contains multiple computing power intervals and their levels. Each computing power characteristic can be used as the computing power level according to the level of the computing power interval to which the computing power range belongs, and multiple computing power levels can be obtained; finally, all resource blocks are divided into multiple computing power resource nodes according to each computing power level. This can be achieved in the following way: for each computing power level, the set of all resource blocks in the computing power interval corresponding to the level of the computing power level is obtained as the computing power resource node of the computing power level, and multiple computing power resource nodes can be obtained.

[0075] It should be noted that in this application, computing power resource nodes refer to nodes that provide computing power in the blockchain network; resource blocks refer to computing power blocks in the blockchain used to complete computing tasks; they refer to nodes responsible for storing, verifying and disseminating block data in the blockchain network; computing power characteristics refer to the capability characteristics of computing power resource nodes when performing computing tasks; computing power levels refer to the level division of computing power resource nodes in terms of providing computing power.

[0076] In some embodiments, determining the resource sharing relationship between various computing resource nodes may be achieved by using the following steps:

[0077] For each computing resource node, obtain all shared blocks between the computing resource node and other computing resource nodes;

[0078] Determine the topological relationship graph of computing power resource nodes through all shared blocks, and then obtain the topological relationship graph of each computing power resource node;

[0079] Generate resource sharing relationships between various computing resource nodes based on all topological association graphs.

[0080] It should be noted that, in this application, the resource sharing relationship is a relationship diagram that measures the ability of cooperation between nodes and the efficiency of resource transfer; in specific implementation, first, for each computing resource node, all shared blocks between the computing resource node and other computing resource nodes are obtained, which can be implemented in the following way, namely: for each computing resource node, the parts that are repeated with the resource blocks in the computing resource node are filtered out from all resource blocks in other computing resource nodes as all shared blocks; then, the topological association diagram of the computing resource node is determined through all shared blocks, and then the topological association diagram of each computing resource node is obtained. For example, the following method can be used to implement it, namely: all shared blocks can be arranged according to the communication coordinates in the blockchain. After being listed and connected, the topological association diagram of the computing resource nodes can be obtained by the above method, wherein the topological association diagram is a network layout diagram that reflects the dependency relationship between nodes in the process of resource sharing and task offloading; in the topological association diagram, the number of resource blocks in the repeated part between each two computing resource nodes can be used as the association value of the corresponding connection in the topological association diagram; finally, the resource sharing relationship between each computing resource node is generated according to all the topological association diagrams. The following method can be used to achieve this, namely: all the topological association diagrams are fused with the same coordinates according to the communication coordinates in the blockchain, so that the fused topological association diagram can be used as the resource sharing relationship between each computing resource node.

[0081] In some embodiments, determining the privacy protection cost of each computing resource node for a privacy-preserving computing task based on the resource sharing relationship and the privacy protection characteristics of the blockchain can be achieved by the following steps:

[0082] For each computing resource node, extract the resource sharing characteristics of the computing resource node from the resource sharing relationship;

[0083] Extract the trust of computing resource nodes in privacy computing tasks from the privacy protection features in the blockchain;

[0084] By associating the trust degree through the resource sharing feature, the privacy protection cost of the computing power resource node for the privacy computing task is obtained, and then the privacy protection cost of each computing power resource node for the privacy computing task is obtained.

[0085] In the specific implementation, first, for each computing power resource node, the resource sharing characteristics of the computing power resource node are extracted from the resource sharing relationship, which can be achieved in the following way, namely: for each computing power resource node, the set of all associated values ​​corresponding to the computing power resource node in the topological association graph in the resource sharing relationship can be used as the resource sharing characteristics of the computing power resource node; then, the trust of the computing power resource node in the privacy computing task is extracted from the privacy protection characteristics in the blockchain, which can be achieved in the following way, namely: the set of privacy protection means of each computing power resource node in the blockchain can be obtained from the control center of the blockchain as the privacy protection feature in the blockchain. The privacy protection means include homomorphic encryption, multi-party computing, zero-knowledge proof and other privacy protection technologies, so that the protection degree of the computing power resource node using the privacy protection technology in the privacy protection feature can be quantified, and then the quantified protection degree can be used as the computing power resource node's privacy protection. The trust level of the private computing task. In other embodiments, in order to improve the environmental adaptability of the trust level, the trust level can also be adjusted in combination with historical experience, which is not limited here. Finally, the trust level is associated with the resource sharing feature to obtain the privacy protection cost of the computing power resource node for the privacy computing task, and then the privacy protection cost of each computing power resource node for the privacy computing task can be obtained. This can be achieved by constructing a multivariate regression model, combining and associating the resource sharing feature with the trust level, and calculating the privacy protection cost of the computing power resource node for the privacy computing task, that is, the privacy protection cost of the computing power resource node for the privacy computing task = a*resource sharing feature + b*trust level + c, where a, b, and c are the parameters of the multivariate regression model, which are obtained by training with historical operation data. The privacy protection cost of each computing power resource node for the privacy computing task can be obtained in the above manner.

[0086] It should be noted that in this application, the privacy protection cost represents the computing power required by the computing resource node to perform privacy protection on the privacy computing task; the resource sharing feature represents the degree of resource sharing between the computing resource nodes; and the trust level represents the degree of trust that the privacy computing task has in the computing resource node.

[0087] In step 104, the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated using various privacy protection costs and the elastic computing power to obtain a balance value of scheduling conflicts in the blockchain. Based on the balance value of scheduling conflicts, privacy computing tasks are evenly distributed among the computing power resource nodes in the blockchain.

[0088] In some embodiments, the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated by each privacy protection cost and the elastic computing power, and the equilibrium value of the scheduling conflict in the blockchain is obtained. Figure 3As described above, the figure is a schematic diagram of a process for determining a balance value of a scheduling conflict in some embodiments of the present application. In this embodiment, determining the balance value of a scheduling conflict can be implemented using the following steps:

[0089] In step 1041, for each computing resource node, a scalable mean value of computing tasks in the computing resource node is extracted from the elastic computing power;

[0090] In step 1042, a conflict value between computing power allocation and privacy protection in the computing power resource node is determined based on the privacy protection cost of the computing power resource node for the privacy computing task and the scalable mean, thereby obtaining a conflict value between computing power allocation and privacy protection in each computing power resource node;

[0091] In step 1043, a balance value of scheduling conflicts in the blockchain is determined based on all conflict values.

[0092] In specific implementation, first, for each computing power resource node, the scalable value of the computing task in the computing power resource node is extracted from the elastic computing power, which can be implemented in the following manner, namely: all computing tasks being executed in the computing power resource node are obtained, and the average of the scalable values ​​of all computing tasks can be used as the scalable average of the computing tasks in the computing power resource node; then, the conflict value between the computing power allocation and privacy protection in the computing power resource node is determined by the privacy protection cost of the computing power resource node for the privacy computing task and the scalable average, and then the conflict value between the computing power allocation and privacy protection in each computing power resource node is obtained. The conflict value between the computing power allocation and privacy protection in each computing power resource node can be obtained in the following manner, namely: the inverse of the product of the scalable average and the privacy protection cost can be used as the conflict value between the computing power allocation and privacy protection in the computing power resource node. The conflict value between the computing power allocation and privacy protection in each computing power resource node can be obtained by the above method; finally, the balance value of the scheduling conflict in the blockchain is determined according to all the conflict values, which can be implemented in the following manner, namely: the variance of all conflict values ​​can be used as the balance value of the scheduling conflict in the blockchain.

[0093] It should be noted that in this application, the balance value represents the degree of balance between the conflict between task privacy protection and computing power resource allocation; the scalability mean represents the average scalability of the computing power resource node when processing tasks; and the conflict value represents the degree of mismatch between the task privacy protection requirements and the computing power resource allocation.

[0094] In some embodiments, the balanced distribution of privacy computing tasks to each computing resource node in the blockchain based on the balance value of the scheduling conflict can be achieved in the following manner, namely: a preset balance threshold can be obtained from the control center of the blockchain. When the balance value of the scheduling conflict is lower than the preset balance threshold, the tasks in the resource block with the highest computing power usage in each computing resource node are unloaded and allocated to the resource block with the lowest computing power usage in each computing resource node until the balance value of the scheduling conflict is greater than or equal to the preset balance threshold.

[0095] In addition, in another aspect of the present application, in some embodiments, the present application provides a privacy computing task scheduling system based on blockchain, referring to Figure 4 This figure is a schematic diagram of the structure of a blockchain-based privacy-preserving computing task scheduling system according to some embodiments of the present application. The blockchain-based privacy-preserving computing task scheduling system includes: a monitoring module 201, a processing module 202, and an execution module 203, which are described as follows:

[0096] Monitoring module 201: In this application, monitoring module 201 is mainly used to monitor the task progress information of the privacy-preserving computing task in the blockchain in real time when the blockchain executes the privacy-preserving computing task;

[0097] Processing module 202, in this application, is used to extract the execution status of the privacy-preserving computing task in the blockchain from the task progress information, and then determine the elastic computing power of the scheduling resources in the blockchain for privacy protection based on the execution status and the task offloading strategy of the computing resource scheduling in the blockchain;

[0098] It should be noted that the processing module 202 is further configured to divide the computing resources in the blockchain into multiple computing resource nodes using a hierarchical structure, thereby determining the resource sharing relationship between each computing resource node, and determining the privacy protection cost of each computing resource node for the privacy computing task based on the resource sharing relationship and the privacy protection characteristics of the blockchain;

[0099] Execution module 203. In this application, execution module 203 is mainly used to evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain through various privacy protection costs and the elastic computing power, obtain the balance value of scheduling conflicts in the blockchain, and evenly distribute privacy computing tasks to various computing power resource nodes in the blockchain based on the balance value of the scheduling conflicts.

[0100] The above describes in detail the examples of the blockchain-based privacy computing task scheduling method and system provided by the embodiments of the present application. It can be understood that in order to implement the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned blockchain-based privacy computing task scheduling method.

[0102] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device for implementing a privacy computing task scheduling method based on blockchain according to an embodiment of the present application. The privacy computing task scheduling method based on blockchain described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0103] The processor 301 may be a general-purpose processor or a dedicated processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data from the software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0104] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0105] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0106] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read data stored in the memory 302. The data can be stored at the same storage address as the program 304, or at a different storage address from the program 304.

[0107] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0108] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] For example, in some embodiments, the present application also provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned blockchain-based privacy computing task scheduling method when executing.

[0111] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0112] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A privacy computing task scheduling method based on blockchain, characterized in that: The steps include: When executing privacy computing tasks on the blockchain, real-time monitoring of the task progress information of privacy computing tasks on the blockchain; Extracting the execution status of the privacy-preserving computing task in the blockchain from the task progress information, and then determining the elastic computing power of the scheduling resources in the blockchain for privacy protection based on the execution status and the task offloading strategy of the computing resource scheduling in the blockchain, where the elastic computing power represents the computing capacity of the computing resource node that can schedule tasks without degrading performance when processing computing tasks; Using a hierarchical structure, the computing resources in the blockchain are divided into multiple computing resource nodes, and the resource sharing relationship between each computing resource node is determined. Based on the resource sharing relationship and the privacy protection characteristics of the blockchain, the privacy protection cost of each computing resource node for the privacy-preserving computing task is determined, where the privacy protection cost represents the computing power required by the computing resource node to protect the privacy of the privacy-preserving computing task. The conflict between computing power allocation and privacy protection in the blockchain is evaluated using various privacy protection costs and the elastic computing power to obtain a balance value of scheduling conflicts in the blockchain. Based on the balance value of scheduling conflicts, privacy computing tasks are evenly distributed among the computing power resource nodes in the blockchain. The balance value of scheduling conflicts represents the degree of balance between task privacy protection and computing power resource allocation conflicts. Determining the resource sharing relationship between various computing resource nodes specifically includes: For each computing resource node, obtain all shared blocks between the computing resource node and other computing resource nodes; Determine the topological relationship graph of computing power resource nodes through all shared blocks, and then obtain the topological relationship graph of each computing power resource node; Generate resource sharing relationships between various computing resource nodes based on all topological association graphs; The privacy protection cost of each computing resource node for the privacy computing task is determined based on the resource sharing relationship and the privacy protection characteristics of the blockchain, specifically including: For each computing resource node, extract the resource sharing characteristics of the computing resource node from the resource sharing relationship; Extracting the trust of computing resource nodes in privacy-preserving computing tasks from the privacy-preserving features of the blockchain. The privacy-preserving features refer to the set of privacy-preserving measures used by each computing resource node in the blockchain. The degree of protection provided by the computing resource node using privacy-preserving techniques in the privacy-preserving features is used as the trust of the computing resource node in the privacy-preserving computing tasks. A multivariate regression model is constructed to combine resource sharing characteristics and trust, and calculate the privacy protection cost of computing power resource nodes for privacy computing tasks.

2. The method according to claim 1, wherein Extracting the execution status of the privacy computing task in the blockchain from the task progress information specifically includes: Get all the computing tasks being executed in the blockchain; For each computing task, extracting the task completion progress and task completion speed of the computing task from the task progress information; Determining the execution characteristics of the computing tasks by the task completion progress and the task completion speed, thereby obtaining the execution characteristics of each computing task; Extract the execution status of privacy computing tasks in the blockchain from all execution features.

3. The method according to claim 1, wherein Determining the elastic computing power of the scheduling resources for privacy protection in the blockchain through the execution state and the task offloading strategy of the computing power resource scheduling in the blockchain specifically includes: Obtain task offloading strategies for computing resource scheduling in blockchain; For each computing task, extracting an execution state value of the computing task from the execution state; The execution state value is evaluated for scalability by the task offloading strategy to obtain a scalable value of the computing power in the computing task, and then the scalable value of the computing power in each computing task is obtained. In a specific implementation, a computing power evaluation model based on a neural network is initialized, the task offloading strategy is used as the computing power release strategy of the computing task in the computing power evaluation model, the execution state value is used as the output label in the computing power evaluation model, the computing power evaluation model is used to evaluate the scalable computing power in the computing task, and the evaluation result of the computing power evaluation model is used as the scalable value of the computing power in the computing task, thereby obtaining the scalable value of the computing power in each computing task; Determine the elastic computing power of scheduling resources for privacy protection in the blockchain based on all scalable values.

4. The method according to claim 1, wherein Using a hierarchical structure to divide the computing power resources in the blockchain into multiple computing power resource nodes specifically includes: Obtain all resource blocks of the blockchain and determine the computing power characteristics of each resource block; All computing power features are classified into different levels through a hierarchical structure to obtain multiple computing power levels. All resource blocks are divided into multiple computing resource nodes according to various computing power levels.

5. The method according to claim 1, wherein The privacy computing task is a distributed computing task based on homomorphic privacy encryption.

6. A blockchain-based privacy computing task scheduling system, which uses the method described in any one of claims 1 to 5 to schedule privacy computing tasks based on blockchain, characterized in that: The system includes: A monitoring module is used to monitor the progress of privacy-preserving computing tasks in real time when the blockchain is executing them. A processing module, configured to extract the execution status of the privacy-preserving computing task in the blockchain from the task progress information, and then determine the elastic computing power of the scheduling resources in the blockchain for privacy protection based on the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain; The processing module is further configured to divide the computing resources in the blockchain into a plurality of computing resource nodes using a hierarchical structure, thereby determining a resource sharing relationship between the computing resource nodes, and determining a privacy protection cost of each computing resource node for a privacy-preserving computing task based on the resource sharing relationship and the privacy protection characteristics of the blockchain; An execution module is used to evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain using various privacy protection costs and the elastic computing power, obtain a balance value of scheduling conflicts in the blockchain, and evenly distribute privacy computing tasks to various computing power resource nodes in the blockchain based on the balance value of scheduling conflicts.

7. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the blockchain-based privacy computing task scheduling method described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes. When the instructions or codes are executed on a computer, the computer implements the blockchain-based privacy computing task scheduling method as described in any one of claims 1 to 5.

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