A blockchain-based joint computing task processing method and device

Abstract

The application provides a kind of joint computing task processing method and device based on blockchain, it is related to blockchain technical field, it can be used in financial field or other technical field.The method comprises the following steps: receiving joint computing task broadcast message;If it is determined that local node ID is contained in task execution participant node ID, then judge whether chain off data is needed to participate in joint computing task, if it is judged as no, then data desensitization is carried out according to data desensitization contract;After completing full-chain consensus, data alignment is carried out according to data alignment contract, after completing full-chain consensus, multi-round computing task is completed using local alignment desensitization data locally according to joint computing contract, after completing each round of computing task, the intermediate calculation result is broadcasted through blockchain and full-chain consensus is carried out, until the preset joint computing termination condition is reached.The device executes the above method.The method and device provided in the embodiment of the application can improve the joint computing efficiency of blockchain.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and more specifically to a blockchain-based collaborative computing task processing method and apparatus. Background Technology

[0002] In existing methods for handling classified information, business users encrypt classified business data locally using homomorphic encryption algorithms to generate ciphertext, and then store the ciphertext on the blockchain for evidence. The business user initiates a multi-party secure privacy computation service request, and the service provider calls the multi-party secure privacy computation service based on the request, returning the task ID and task status. The multi-party secure privacy computation service performs ciphertext computation on the ciphertext transmitted by multiple business users based on homomorphic encryption algorithms, and stores the task ID and ciphertext computation results on the blockchain for evidence. The business user queries and retrieves the ciphertext computation results, decrypts the data, and views the results. In this process, homomorphic ciphertext computation is performed on classified data during the computation process, preventing data leakage and ensuring that each party cannot obtain relevant data other than their own. Furthermore, the encryption process of the classified data is completed locally, and decryption is not required during multi-party computation, avoiding the additional computational cost incurred by encryption and decryption. Secondly, the relevant data computation tasks are transferred to a multi-party secure and privacy-preserving computation service. This service is independent of the business users and the underlying evidence storage service, ensuring the independence and trustworthiness of the intermediate computation service. The computation result of the encrypted data is returned in encrypted form and requires the user's private key to decrypt it, further preventing the leakage of computation results of relevant confidential data. Both the encrypted data and the encrypted computation result of the data are stored on the blockchain for evidence preservation, ensuring traceability and preventing malicious data tampering.

[0003] However, the above technical solutions merely use blockchain as a tool for data storage in the privacy computing process. The data is processed on the blockchain and then uploaded to the chain, which does not make full use of blockchain, resulting in poor efficiency for the business processing task executor in data fusion and computing. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a blockchain-based collaborative computing task processing method and apparatus, which can at least partially solve the problems existing in the prior art.

[0005] On one hand, this invention proposes a blockchain-based collaborative computing task processing method, comprising:

[0006] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0007] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0008] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0009] The step of broadcasting the intermediate calculation results via blockchain and achieving full-chain consensus after each round of calculation tasks includes:

[0010] After each round of computation is completed, the intermediate computation results from all other task execution participants are received via the blockchain and used as parameters for the next round of computation or to adjust the local model.

[0011] The blockchain-based collaborative computing task processing method further includes:

[0012] If the determination is yes, then obtain off-chain data, and then perform data anonymization on the on-chain and off-chain data according to the data anonymization contract.

[0013] The acquisition of off-chain data includes:

[0014] Obtain off-chain data through oracle contracts and oracle components.

[0015] The receiving of the joint computing task broadcast message includes:

[0016] Receive broadcast messages for joint computing tasks initiated by participating nodes.

[0017] The blockchain-based joint computing task processing method further includes the following steps after broadcasting the intermediate computation results via blockchain and achieving full-chain consensus until the preset joint computing termination condition is met:

[0018] After completing the final round of computation and broadcasting the intermediate computation results after reaching consensus, a notification message is issued indicating the end of the joint computation task.

[0019] On one hand, this invention proposes a blockchain-based collaborative computing task processing device, comprising:

[0020] A receiving unit is used to receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract and the node IDs of the task execution participants, and the joint computing contract includes a preset joint computing termination condition;

[0021] The judgment unit is used to determine whether off-chain data is needed to participate in the joint computing task if it is determined that the local node ID is included in the node ID of the task execution participant; if the determination is no, data desensitization is performed according to the data desensitization contract.

[0022] The computing unit is used to perform data alignment according to the data alignment contract after the full-chain consensus is completed. After the full-chain consensus is completed, it uses the locally aligned and desensitized data to complete multiple rounds of computing tasks locally according to the joint computing contract. After each round of computing tasks is completed, the intermediate computing results are broadcast through the blockchain and the full-chain consensus is achieved until the preset joint computing termination condition is reached.

[0023] In another aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the following method:

[0024] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0025] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0026] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0027] This invention provides a computer-readable storage medium, comprising:

[0028] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method:

[0029] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0030] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0031] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0032] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the following method:

[0033] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0034] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0035] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0036] The present invention provides a blockchain-based collaborative computing task processing method and apparatus, which receives a collaborative computing task broadcast message. The broadcast message carries a data anonymization contract, a data alignment contract, a collaborative computing contract, and task execution participant node IDs. The collaborative computing contract includes a preset collaborative computing termination condition. If the local node ID is found to be included in the task execution participant node IDs, it is determined whether off-chain data is needed to participate in the collaborative computing task. If not, data anonymization is performed according to the data anonymization contract. After achieving full-chain consensus, data alignment is performed according to the data alignment contract. After achieving full-chain consensus, multiple rounds of computation tasks are completed locally using the locally aligned and anonymized data according to the collaborative computing contract. After each round of computation tasks is completed, the intermediate computation results are broadcast via the blockchain and full-chain consensus is achieved until the preset collaborative computing termination condition is reached. This improves the collaborative computing efficiency of the blockchain. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0038] Figure 1 This is a flowchart illustrating a blockchain-based collaborative computing task processing method according to an embodiment of the present invention.

[0039] Figure 2 This is a hierarchical structure diagram of the blockchain-based collaborative computing task processing method provided in this embodiment of the invention.

[0040] Figure 3 This is a flowchart illustrating a blockchain-based collaborative computing task processing method according to another embodiment of the present invention.

[0041] Figure 4 This is a schematic diagram of the structure of a blockchain-based collaborative computing task processing device provided in an embodiment of the present invention.

[0042] Figure 5 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0044] Explanation of relevant terms:

[0045] Blockchain:

[0046] Blockchain is a term in the field of information technology. Essentially, it is a shared database where data or information is stored, possessing characteristics such as "unforgeable," "fully traceable," "transparent," and "collectively maintained." Based on these characteristics, blockchain technology lays a solid foundation of trust, creates a reliable "cooperation" mechanism, and has broad application prospects.

[0047] In the blockchain database:

[0048] Data can only be added in the form of blocks through a consensus algorithm, and cannot be modified or deleted to prevent tampering;

[0049] Each block will contain at least one block generation time and block signature;

[0050] All transaction data will be signed by both parties to prevent repudiation;

[0051] In traditional blockchains, a new block stores the hash of the previous block and is linked to the previous block through this hash;

[0052] In a blockchain multi-node network:

[0053] All nodes have permission to browse blocks, but cannot have complete control over the blocks;

[0054] All nodes have the power to verify blocks, participate in consensus, and increase their data rights through consensus.

[0055] This can be achieved through blockchain:

[0056] Data records and on-chain data traceability that do not rely on trusted third parties;

[0057] Data communication and trusted value exchange through peer-to-peer networks;

[0058] It has a very strong resistance to all attacks aimed at the system's central controller.

[0059] Hyperledger Fabric:

[0060] Hyperledger Fabric is an open-source, enterprise-grade, licensed distributed ledger technology (DLT) platform, which differs from other popular DLT or blockchain platforms in several key ways.

[0061] Hyperledger was established by the Linux Foundation, which itself has a very long and successful history of open source projects. Hyperledger is managed by multiple technical councils, and the Hyperledger Fabric project is maintained by people from various organizations. Its developer community includes 35 organizations and nearly 200 developers.

[0062] Privacy Computing:

[0063] The term "privacy computing" was formally introduced in the 2016 report "Research Scope and Development Trends of Privacy Computing". It defines privacy computing as: "Computational theories and methods for the protection of privacy information throughout its entire lifecycle. It is a computable model and axiomatic system for privacy measurement, privacy leakage costs, and the complexity of privacy protection and privacy analysis when the ownership, management and use rights of privacy information are separated."

[0064] Privacy computing is essentially about solving data service issues such as data circulation and data application while protecting data privacy.

[0065] The concept of privacy computing includes:

[0066] Data is available but not visible; the model moves even when the data is stationary; data is available but not visible; data is controllable and measurable; data is not shared, but its value is shared, etc.

[0067] Based on the main technologies currently available in the market related to privacy computing, they can be divided into three categories: (differential privacy, as a data processing method, is also included):

[0068] Protocol-based secure multi-party computation;

[0069] Federated learning based on modern cryptography;

[0070] Hardware-based trusted execution environment.

[0071] Secure multi-party computation:

[0072] Secure multi-party computation is a technique and system for securely computing agreed-upon functions without the parties sharing their own data and without a trusted third party. Through secure algorithms and protocols, parties encrypt or transform their plaintext data before providing it to other parties. No single party can access the plaintext data of other parties, thus ensuring the security of all parties' data.

[0073] Federated Learning:

[0074] Federated learning is a distributed machine learning technique and system that involves two or more parties who conduct joint machine learning through secure algorithmic protocols. It allows for joint modeling and the provision of model inference and prediction services by exchanging intermediate data, without requiring any data to leave their local locations. Moreover, the performance of models obtained in this way is almost identical to that of traditional centralized machine learning models.

[0075] Currently, federated learning technology is relatively mature in traditional machine learning algorithms such as linear regression and decision trees, while the focus of research is on deep learning models.

[0076] The application of federated learning technology typically requires integration with secure multi-party computation techniques, and even blockchain. The future direction of federated technology development is to build a unified federated platform for executing data transactions.

[0077] Oracles:

[0078] 1. Introduction to Oracles

[0079] Blockchain is a deterministic, closed system that cannot actively acquire data from outside the chain. Oracles serve as the interface between blockchain smart contracts and the external world, acting as a bridge for communication between the blockchain and the outside world.

[0080] The primary function of oracles is to provide reliable external data for smart contracts. Their core value lies in connecting the blockchain with the outside world based on trust. Ideally, the workflow of an oracle is to accept requests, retrieve data, and return data. Data sources available for oracles to query typically include internet URLs, search engines, data from other blockchains, and data from the InterPlanetary File System (IPFS).

[0081] 2. Introduction to relevant concepts

[0082] (1) Oracle

[0083] An oracle is a tool that provides trusted data to the blockchain. Its core value lies in connecting the blockchain with the outside world based on trust. Oracles come in various types, and can be classified by hardware / software, centralization, and data flow. When designing oracles for practical applications, it is crucial to consider issues such as confidentiality, integrity, and availability.

[0084] (2) Trusted Execution Environment

[0085] A Trusted Execution Environment (TEE) is a secure region built within a central processing unit (CPU) using hardware and software methods to ensure the confidentiality and integrity of programs and data loaded within it. A trusted CPU typically refers to a commercially available CPU chip with a pre-integrated trusted execution control unit; only some newly developed chips support TEE.

[0086] The fundamental principle of TEE (Trusted Execution Environment) is to divide the system's hardware and software resources into two execution environments: a trusted execution environment and a normal execution environment. These two environments are securely isolated, with independent internal data pathways and storage space required for computation. Applications in the normal execution environment cannot access the TEE; even within the TEE, the operation of multiple applications is independent and they cannot access each other without authorization.

[0087] TEEs are typically used for operations with high security requirements, protecting sensitive data, and protecting high-value data, such as:

[0088] 1. High-security operations: such as secure keyboard password input, fingerprint input, user authentication, and mobile payment;

[0089] 2. Safeguard confidential and sensitive data: such as user certificate private keys and fingerprint data;

[0090] 3. Content security: such as DRM (Digital Rights Management).

[0091] (3) Introduction to Trusted Execution Environment Project

[0092] The Asylo open-source framework only supports C / C++, with Java support starting soon. It calls C / C++ via JNI and can be configured with SGX. SGX is usually disabled by default on most machines and is generally used in enterprise applications.

[0093] Inclave provides the Enclave feature for security-enhanced instances like ECS, and the corresponding SDK is configured for development.

[0094] Hyperledger Avalon reduces on-chain transaction performance by extending transactions to off-chain trusted environments, enabling Trusted Environments (TEEs), Hybrid Computation (MPC), and zero-knowledge proofs.

[0095] SCONE is a container-based trusted computing environment that supports almost all programming languages ​​and Kubernetes clusters. Data within containers is encrypted in memory. It has three modes: Sim, Hardware, and Auto. Sim mode does not call SGX but uses the local CPU to simulate a trusted environment. Hardware mode relies on hardware support; the location of the installed SGX driver is mounted when the container runs.

[0096] (4) System chaincode

[0097] System chaincode is a special type of chaincode that runs inside the peer container. Unlike ordinary chaincode that runs in a separate container, system chaincode can access resources within the peer container and can be used to perform special operations. System chaincode is automatically registered and deployed when the peer starts, therefore it cannot be installed, instantiated, or invoked through the SDK. System chaincode can be configured as a plugin. To upgrade system chaincode, simply recompile the peer image and deploy it.

[0098] Figure 1 This is a flowchart illustrating a blockchain-based collaborative computing task processing method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the blockchain-based collaborative computing task processing method provided in this embodiment of the invention includes:

[0099] Step S1: Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants. The joint computing contract includes preset joint computing termination conditions.

[0100] Step S2: If it is determined that the local node ID is included in the node ID of the task execution participant, then determine whether off-chain data is needed to participate in the joint computation task. If the determination is no, then perform data desensitization according to the data desensitization contract.

[0101] Step S3: After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0102] In step S1 above, the device receives a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants. The joint computing contract includes preset joint computing termination conditions. The device can be a computer device, such as a server, that executes the method. The acquisition, storage, use, and processing of data in this application's technical solution all comply with relevant national laws and regulations. The preset joint computing termination conditions may include a pre-set number of joint computing rounds or a threshold, and are not specifically limited.

[0103] like Figure 2 As shown, the layered structure of the embodiments of the present invention is described below:

[0104] 1. On-chain data is based on fusion computing that protects data privacy:

[0105] (1) Peer nodes introduce privacy computing functionality-related modules, such as... Figure 2 The cryptographic components, machine learning algorithm components, and data anonymization components are integrated, and the relevant algorithms are provided for the smart contract module of the Peer node to call.

[0106] (2) Modify the Peer node smart contract engine: introduce contracts such as data anonymization, joint computation, and joint modeling. The designed contract realizes the privacy computing function by interacting with the privacy computing function module.

[0107] 2. Collaborative computation of on-chain and off-chain data:

[0108] (1) Peer nodes introduce data coordination components, such as oracles, and when necessary, introduce off-chain data to perform multi-party data fusion calculations in collaboration with on-chain data;

[0109] (2) Modify the Peer node smart contract engine and introduce oracle contracts to interact with off-chain data through oracles;

[0110] The general idea is to import off-chain data onto the blockchain and then perform the fusion computing task on the blockchain.

[0111] like Figure 3 As shown, the process prior to the step where each participating node in the task execution receives the broadcast message for the joint computation task includes:

[0112] 1. The participating nodes initiate a joint computation task.

[0113] 2. The task initiating participant node broadcasts the joint computing task to each task execution participant node through the blockchain. The broadcast message specifies information such as the data anonymization contract, data alignment contract, joint computing contract, and task execution participant node ID.

[0114] In step S2 above, if the device determines that the local node ID is included in the task execution participant node IDs, it determines whether off-chain data is needed to participate in the joint computation task. If the determination is no, data anonymization is performed according to the data anonymization contract. Since the task execution participant node IDs include multiple task execution participant node IDs specified by the task initiating participant node, as long as the ID of the current task execution participant node receiving the joint computation task broadcast message is among the multiple task execution participant node IDs, it indicates that this node is one of the task execution nodes specified by the task initiating participant node.

[0115] like Figure 3 The process shown also includes:

[0116] Determine whether off-chain data is needed to participate in the joint computation task. If the determination is no, proceed as follows:

[0117] 3. Each participating node in the task execution performs data anonymization according to the data anonymization contract. After the whole chain consensus is completed, the data alignment task is triggered.

[0118] If the determination is yes, then the following process is executed:

[0119] 4. Each participating node in the task execution obtains off-chain data through the oracle contract and oracle component, and then performs data anonymization on the on-chain and obtained off-chain data according to the data anonymization contract. After the whole chain consensus is completed, the data alignment task is triggered.

[0120] It should be noted that, by default, the off-chain data is data that is related to or that the participant has the right to access.

[0121] In step S3 above, after the device completes the full-chain consensus, it performs data alignment according to the data alignment contract. After the full-chain consensus is completed, it completes multiple rounds of computation tasks locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0122] like Figure 3 The process shown also includes:

[0123] 5. Each participating node in the task execution performs data alignment according to the data alignment contract. After achieving full-chain consensus, the joint computation task is triggered.

[0124] 6. Each participating node in the task execution process completes multiple rounds of computation tasks locally using locally aligned and anonymized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate results calculated are broadcast through the blockchain and consensus is reached across the entire chain until the number of rounds or thresholds set by the joint computation contract are reached.

[0125] The step of broadcasting the intermediate computation results via blockchain and achieving full-chain consensus after each round of computation is completed includes:

[0126] After each round of computation is completed, the intermediate computation results from all other task execution participants are received via the blockchain and used as parameters for the next round of computation or to adjust the local model.

[0127] like Figure 3 The process shown also includes:

[0128] 7. After completing the final round of computation and broadcasting the intermediate results after consensus, each participating node in the task execution publishes a notification message indicating the end of the joint computation task, thus concluding the task. All participating nodes receive the final computation result. It should be noted that the recipient of the notification message can be specified in the joint computation contract, and the recipient of the intermediate results for each round can also be specified.

[0129] This invention provides a blockchain-based multi-party data fusion computing framework and method. On the one hand, it solves the problem of directly utilizing the blockchain network to perform multi-party data fusion computing on-chain based on detailed data on the blockchain, while protecting data privacy. On the other hand, it solves the problem of simultaneously introducing off-chain data and on-chain data for collaborative computing when necessary.

[0130] The blockchain-based collaborative computing task processing method provided in this embodiment of the invention has the following beneficial effects:

[0131] 1. Solve the problem of data silos on the blockchain.

[0132] 2. Solve the problem of data traceability.

[0133] 3. On-chain and off-chain coordinated computation improves the performance of data fusion computation.

[0134] 4. Standardize the integration and use of on-chain data and implement refined authorization: De-identify some on-chain data to form sample data, which will be used by various participants in the process of building the privacy computing model for data exploration and alignment, as well as for model testing.

[0135] The blockchain-based federated computing task processing method provided in this embodiment of the invention receives a federated computing task broadcast message. The broadcast message carries a data anonymization contract, a data alignment contract, a federated computing contract, and the node IDs of the task execution participants. The federated computing contract includes a preset federated computing termination condition. If it is determined that the local node ID is included in the task execution participant node IDs, it is determined whether off-chain data is needed to participate in the federated computing task. If not, data anonymization is performed according to the data anonymization contract. After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computing tasks are completed locally using the locally aligned and anonymized data according to the federated computing contract. After each round of computing tasks is completed, the intermediate computing results are broadcast through the blockchain and full-chain consensus is achieved until the preset federated computing termination condition is reached. This method can improve the federated computing efficiency of the blockchain.

[0136] Furthermore, the step of broadcasting the intermediate computation results via blockchain and achieving full-chain consensus after each round of computation is completed includes:

[0137] After each round of computation is completed, the intermediate computation results from all other participating nodes are received via the blockchain. These results are used as parameters for the next round of computation or to adjust the local model. This is explained above and will not be repeated here.

[0138] Furthermore, the blockchain-based collaborative computing task processing method also includes:

[0139] If the determination is yes, then off-chain data is obtained, and then data anonymization is performed on both on-chain and off-chain data according to the aforementioned data anonymization contract. Refer to the above explanation; further details are omitted.

[0140] Furthermore, the acquisition of off-chain data includes:

[0141] Obtain off-chain data through oracle contracts and oracle components. Refer to the above explanation; further details are omitted.

[0142] Furthermore, receiving the joint computing task broadcast message includes:

[0143] Receive broadcast messages for collaborative computing tasks initiated by participating nodes. Refer to the above explanation; further details are omitted.

[0144] Furthermore, after the step of broadcasting the intermediate computation results calculated via the blockchain and achieving full-chain consensus until the preset joint computation termination condition is met, the blockchain-based joint computation task processing method further includes:

[0145] After completing the final round of computation and broadcasting the intermediate computation results after reaching consensus, a notification message announcing the end of the joint computation task is released. This is similar to the explanation above and will not be repeated here.

[0146] It should be noted that the blockchain-based collaborative computing task processing method provided in this embodiment of the invention can be used in the financial field, or in any technical field other than the financial field. This embodiment of the invention does not limit the application field of the blockchain-based collaborative computing task processing method.

[0147] Figure 4 This is a schematic diagram of the structure of a blockchain-based collaborative computing task processing device according to an embodiment of the present invention, as shown below. Figure 4 As shown, the blockchain-based collaborative computing task processing device provided in this embodiment of the invention includes a receiving unit 401, a judging unit 402, and a computing unit 403, wherein:

[0148] The receiving unit 401 is used to receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants. The joint computing contract includes a preset joint computing termination condition. The judging unit 402 is used to determine whether off-chain data is needed to participate in the joint computing task if it is determined that the local node ID is included in the task execution participant node IDs. If the determination is no, data anonymization is performed according to the data anonymization contract. The computing unit 403 is used to perform data alignment according to the data alignment contract after the full-chain consensus is completed. After the full-chain consensus is completed, the computing unit uses the local aligned and anonymized data to complete multiple rounds of computing tasks locally according to the joint computing contract. After each round of computing tasks is completed, the intermediate computing results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computing termination condition is reached.

[0149] Specifically, the receiving unit 401 in the device is used to receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants. The joint computing contract includes a preset joint computing termination condition. The judging unit 402 is used to determine whether off-chain data is needed to participate in the joint computing task if it is determined that the local node ID is included in the task execution participant node IDs. If the determination is no, data anonymization is performed according to the data anonymization contract. The computing unit 403 is used to perform data alignment according to the data alignment contract after the full-chain consensus is completed. After the full-chain consensus is completed, multiple rounds of computing tasks are completed locally using locally aligned and anonymized data according to the joint computing contract. After each round of computing tasks is completed, the intermediate computing results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computing termination condition is reached.

[0150] The blockchain-based federated computing task processing device provided in this embodiment of the invention receives a federated computing task broadcast message. The federated computing task broadcast message carries a data anonymization contract, a data alignment contract, a federated computing contract, and the node IDs of the task execution participants. The federated computing contract includes a preset federated computing termination condition. If it is determined that the local node ID is included in the task execution participant node IDs, it is determined whether off-chain data is needed to participate in the federated computing task. If the determination is no, data anonymization is performed according to the data anonymization contract. After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computing tasks are completed locally using the locally aligned and anonymized data according to the federated computing contract. After each round of computing tasks is completed, the intermediate computing results are broadcast through the blockchain and full-chain consensus is achieved until the preset federated computing termination condition is reached, which can improve the federated computing efficiency of the blockchain.

[0151] Furthermore, the computing unit 403 is specifically used for:

[0152] After each round of computation is completed, the intermediate computation results from all other task execution participants are received via the blockchain and used as parameters for the next round of computation or to adjust the local model.

[0153] Furthermore, the blockchain-based federated computing task processing device is also used for:

[0154] If the determination is yes, then obtain off-chain data, and then perform data anonymization on the on-chain and off-chain data according to the data anonymization contract.

[0155] Furthermore, the blockchain-based federated computing task processing device is also specifically used for:

[0156] Obtain off-chain data through oracle contracts and oracle components.

[0157] Furthermore, the receiving unit 401 is specifically used for:

[0158] Receive broadcast messages for joint computing tasks initiated by participating nodes.

[0159] Furthermore, after the step of broadcasting the intermediate computation results calculated via the blockchain and achieving full-chain consensus until the preset joint computation termination condition is met, the blockchain-based joint computation task processing device is further configured to:

[0160] After completing the final round of computation and broadcasting the intermediate computation results after reaching consensus, a notification message is issued indicating the end of the joint computation task.

[0161] The embodiments of the present invention provide a blockchain-based federated computing task processing device that can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0162] Figure 5 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention, such as... Figure 5 As shown, the computer device includes: a memory 501, a processor 502, and a computer program stored in the memory 501 and executable on the processor 502. When the processor 502 executes the computer program, it implements the following method:

[0163] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0164] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0165] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0166] This embodiment discloses a computer program product, which includes a computer program that, when executed by a processor, implements the following method:

[0167] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0168] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0169] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0170] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the following method:

[0171] Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions;

[0172] If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract.

[0173] After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

[0174] Compared with existing technical solutions, this invention receives a joint computing task broadcast message. The broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants. The joint computing contract includes a preset joint computing termination condition. If the local node ID is found to be included in the task execution participant node IDs, it is determined whether off-chain data is needed to participate in the joint computing task. If not, data anonymization is performed according to the data anonymization contract. After achieving full-chain consensus, data alignment is performed according to the data alignment contract. After achieving full-chain consensus, multiple rounds of computation tasks are completed locally using the locally aligned and anonymized data according to the joint computing contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain for full-chain consensus until the preset joint computing termination condition is reached. This improves the joint computing efficiency of the blockchain.

[0175] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0177] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0179] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0180] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A blockchain-based collaborative computing task processing method, characterized in that, The blockchain-based collaborative computing task processing method is executed based on a hierarchical structure; The layered structure includes an on-chain / off-chain coordination layer, a privacy computing layer, and a blockchain layer. The on-chain and off-chain coordination layer includes a data coordination component; the privacy computing layer includes a cryptographic component, a machine learning algorithm component, and a data anonymization component; and the blockchain layer includes a smart contract module, a consensus algorithm module, a distributed computing engine, distributed storage, a distributed network, and a distributed database. Receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract, and the node IDs of the task execution participants, and the joint computing contract includes preset joint computing termination conditions; If it is determined that the local node ID is included in the node ID of the task execution participant, then it is determined whether off-chain data is needed to participate in the joint computing task. If the determination is no, then data desensitization is performed according to the data desensitization contract. After the full-chain consensus is completed, data alignment is performed according to the data alignment contract. After the full-chain consensus is completed, multiple rounds of computation tasks are completed locally using locally aligned and desensitized data according to the joint computation contract. After each round of computation tasks is completed, the intermediate computation results are broadcast through the blockchain and full-chain consensus is achieved until the preset joint computation termination condition is reached.

2. The blockchain-based collaborative computing task processing method according to claim 1, characterized in that, The step of broadcasting the intermediate computation results via blockchain and achieving full-chain consensus after each round of computation is completed includes: After each round of computation is completed, the intermediate computation results from all other task execution participants are received via the blockchain and used as parameters for the next round of computation or to adjust the local model.

3. The blockchain-based collaborative computing task processing method according to claim 1, characterized in that, The blockchain-based federated computing task processing method also includes: If the determination is yes, then obtain off-chain data, and then perform data anonymization on the on-chain and off-chain data according to the data anonymization contract.

4. The blockchain-based collaborative computing task processing method according to claim 3, characterized in that, The acquisition of off-chain data includes: Obtain off-chain data through oracle contracts and oracle components.

5. The blockchain-based collaborative computing task processing method according to claim 1, characterized in that, The receiving of the joint computing task broadcast message includes: Receive broadcast messages for joint computing tasks initiated by participating nodes.

6. The blockchain-based collaborative computing task processing method according to claim 1, characterized in that, After the step of broadcasting the intermediate computation results calculated via blockchain and achieving full-chain consensus until the preset joint computation termination condition is met, the blockchain-based joint computation task processing method further includes: After completing the final round of computation and broadcasting the intermediate computation results after reaching consensus, a notification message is issued indicating the end of the joint computation task.

7. A blockchain-based collaborative computing task processing device, characterized in that, The blockchain-based federated computing task processing device is contained in a hierarchical structure; The layered structure includes an on-chain / off-chain coordination layer, a privacy computing layer, and a blockchain layer. The on-chain and off-chain coordination layer includes a data coordination component; the privacy computing layer includes a cryptographic component, a machine learning algorithm component, and a data anonymization component; and the blockchain layer includes a smart contract module, a consensus algorithm module, a distributed computing engine, distributed storage, a distributed network, and a distributed database. A receiving unit is used to receive a joint computing task broadcast message; the joint computing task broadcast message carries a data anonymization contract, a data alignment contract, a joint computing contract and the node IDs of the task execution participants, and the joint computing contract includes a preset joint computing termination condition; The judgment unit is used to determine whether off-chain data is needed to participate in the joint computing task if it is determined that the local node ID is included in the node ID of the task execution participant; if the determination is no, data desensitization is performed according to the data desensitization contract. The computing unit is used to perform data alignment according to the data alignment contract after the full-chain consensus is completed. After the full-chain consensus is completed, it uses the locally aligned and desensitized data to complete multiple rounds of computing tasks locally according to the joint computing contract. After each round of computing tasks is completed, the intermediate computing results are broadcast through the blockchain and the full-chain consensus is achieved until the preset joint computing termination condition is reached.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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