A decentralized artificial intelligence (AI) service network system

Abstract

The present invention relates to a decentralized artificial intelligence (AI) service network method and system, which aims to provide a secure, transparent and efficient data and computing power sharing platform. Through a decentralized storage mechanism, a content-addressed distributed file system such as IPFS is used to store data, combined with homomorphic encryption and secure multi-party computing (SMC) technology to ensure data security and privacy protection. The system includes a computing power resource management unit, which builds a decentralized computing power resource pool, and automatically manages the allocation and use of resources through smart contracts to promote the efficient use of computing power resources. The point incentive mechanism is implemented through a point management unit to reward participants who contribute data and computing power in the network. At the same time, a network governance unit in the form of a decentralized autonomous organization (DAO) is adopted to realize the autonomous governance and decision-making of the network. The application scenarios of the present invention include but are not limited to precision agriculture, personalized medicine, disease diagnosis and other fields. By providing a decentralized AI service network, data sharing is promoted, the innovation and popularization of AI technology is promoted, and data privacy and security are protected at the same time.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence (AI), and in particular to a decentralized AI service network method and system for promoting data sharing, improving data security and privacy protection, and accelerating the provision of AI services by utilizing distributed computing resources. Background Art

[0002] In today's technological context, the rapid development of artificial intelligence (AI) technology is driving changes in all walks of life. However, as AI technology continues to increase its demand for data and computing power, traditional centralized AI systems are facing a series of challenges and problems, which mainly focus on data privacy, security, and the efficiency of computing resource utilization.

[0003] Data silos refer to data that is stored in isolation in different systems, organizations, or platforms and cannot be effectively shared and used together. This not only limits the availability of data, but also hinders the comprehensiveness and depth of AI model training, thereby affecting the accuracy and efficiency of AI applications. The data silo problem is caused by factors such as inconsistent data ownership, privacy policies, and technical standards, and is a major obstacle to the current data management system.

[0004] With the surge in the amount of personal and institutional data, how to protect the security and privacy of this data has become a major challenge. Centralized AI systems usually rely on storing a large amount of sensitive data in a single server or data center, which makes the data easy targets for hacker attacks. Once the data center is attacked, it may lead to large-scale data leaks. In addition, centralized data processing methods also make users lack sufficient control over their own data, exacerbating concerns about privacy protection.

[0005] The training of AI models, especially deep learning models, requires huge computing resources. However, the current centralized computing resources are often concentrated in a few large data centers, resulting in uneven resource allocation and high costs. In addition, a large number of computing resources may be idle at a certain time, resulting in a waste of resources. In addition, for small and medium-sized enterprises and individual developers, the high computing cost has become a major obstacle for them to enter the AI ​​field.

[0006] In the face of the above challenges, a new technical solution is urgently needed to promote the secure sharing of data, protect data privacy, and efficiently utilize the globally dispersed computing resources. Decentralized AI provides a possible path. Through distributed data storage, encryption technology, blockchain technology such as smart contracts, and decentralized computing networks, decentralized AI aims to build a more open, secure and efficient AI service ecosystem. Summary of the invention

[0007] like Figure 1 , Figure 2 The present invention proposes an innovative decentralized AI service network method and system, aiming to overcome the defects of traditional centralized AI systems in data privacy, security, and computing resource utilization efficiency. By combining advanced technical means and the concept of decentralization, the present invention aims to build a safer, more efficient, and fairer AI service ecosystem.

[0008] Technical implementation: This system uses decentralized storage technologies such as IPFS to store data in multiple nodes in the network instead of concentrating it in a single data center. Combining homomorphic encryption and secure multi-party computing technology, the stored data is encrypted to ensure absolute security and privacy protection of the data during storage and use.

[0009] Advantages and Impact: This technical solution not only improves the availability and durability of data, but also effectively avoids the security risks brought by centralized storage, providing strong technical support for data privacy protection.

[0010] Construction method: By establishing a decentralized computing resource pool, this system allows individuals and institutions around the world to contribute their computing resources. Smart contract technology is used to automatically manage the allocation and use of resources, improving the utilization efficiency and accessibility of computing resources.

[0011] Application value: This decentralized computing network greatly lowers the threshold for AI computing, allowing more researchers and developers to participate in the training and deployment of AI models, and promoting the innovation and application of AI technology.

[0012] Implementation plan: By designing a set of incentive mechanisms based on points, the present invention rewards participants who contribute data and computing power to the network. This incentive mechanism ensures the self-sufficiency and sustainable development of the network and stimulates the enthusiasm of community members.

[0013] Social effect: This incentive mechanism effectively promotes the sharing of data and computing resources, and accelerates the construction and development of the decentralized AI ecosystem.

[0014] Protection measures: This invention not only ensures data security through encryption technology, but also introduces technologies such as differential privacy to further protect the privacy of user data. These measures together constitute a multi-level security protection system, which effectively prevents the risk of data leakage and abuse.

[0015] Long-term impact: By ensuring data security and privacy protection, this invention creates a trust environment for users and data providers, promoting data sharing and collaboration.

[0016] Governance mechanism: This invention adopts the form of decentralized autonomous organization (DAO) to realize the democratization and self-organization of network governance. All participants can participate in governance decision-making and jointly determine the development direction and policy adjustments of the network.

[0017] Development prospects: This decentralized governance model not only ensures the transparency and fairness of network operation, but also effectively responds to future challenges and changes and ensures the long-term healthy development of the network.

[0018] In summary, the present invention provides a new network architecture and operation model for AI services through decentralized methods and technological innovations, which is expected to solve many challenges faced by traditional centralized AI systems and promote the advancement and widespread application of AI technology. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 It is a diagram of the composition of a decentralized service network system; Figure 2 Flowchart of services and incentives for AI. DETAILED DESCRIPTION

[0019] The decentralized AI service network system of the present invention achieves the purpose of providing AI services efficiently and safely through the collaborative work of its constituent units. The following is a detailed description of the specific implementation methods and application scenarios of each unit:

[0020] Implementation method: Decentralized storage technologies such as IPFS are used to store agricultural data, medical records, etc. in multiple nodes in the network. Each piece of data is uniquely identified by a hash value to ensure data retrievability and non-tamperability.

[0021] Application scenarios: In the field of precision agriculture, the system can access soil and climate data of farms distributed in different geographical locations, providing a data basis for crop growth analysis over a large area.

[0022] Implementation method: Combining homomorphic encryption and secure multi-party computation (SMC) technology to encrypt sensitive data, allowing calculations to be performed on encrypted data without leaking the original information.

[0023] Application scenarios: In the medical field, encryption technology is used to protect the privacy of patient data, allowing different medical institutions to collaborate in disease diagnosis research without directly sharing the original patient information.

[0024] Implementation method: Create a decentralized computing resource pool, allowing individuals and institutions to contribute their idle computing resources. Use smart contracts to automatically manage resource allocation and computing task execution.

[0025] Application scenarios: Provide necessary computing power support for big data analysis in precision agriculture and complex calculations of medical data to ensure efficient execution of AI services.

[0026] Implementation method: Smart contracts are used to automatically execute transactions and agreements of AI services in a decentralized network. From data usage permissions to computing power leasing, all are automated and transparently managed through blockchain technology.

[0027] Application scenario: Enable data providers and computing power providers to receive fair compensation and promote fair exchange and utilization of resources.

[0028] Implementation method: By issuing tokens as the transaction medium within the network, an incentive mechanism is designed to reward network participants, including data providers, computing power providers and other contributors.

[0029] Application scenarios: In the fields of precision agriculture and medical care, participants can obtain token rewards by contributing data or providing computing services, which encourages more resources to be invested in the decentralized AI service network.

[0030] Implementation method: Decentralized Autonomous Organization (DAO) is adopted to realize the democratization and transparency of network governance. All participants can participate in voting and jointly decide the direction of network development and important matters.

[0031] Application scenarios: When faced with new technical challenges or policy adjustments, community members can vote to decide how to optimize the network structure or adjust the incentive mechanism to ensure the healthy development of the network.

[0032] Through the specific implementation methods of the above-mentioned units, the present invention can effectively utilize distributed data and computing resources while ensuring data privacy and security, and provide efficient and reliable decentralized AI services for all walks of life.

[0033] The decentralized AI service network method and system of the present invention, through technical solutions such as decentralized data storage, encryption technology protection, computing power resource sharing, point incentives and autonomous community governance, can not only promote the secure sharing of data and the efficient use of computing power resources, but also accelerate the innovation and application of AI services, which is of great significance to promoting the development of AI technology.

Claims

1. A decentralized artificial intelligence (AI) service network system, comprising: ① Data storage unit, which uses a decentralized storage mechanism to store data; ② Encryption processing unit, which encrypts the data stored in the data storage unit to ensure the security of data transmission and storage; ③ Computing resource management unit, responsible for collecting and allocating computing resources in the decentralized network; ④Transaction processing unit, which uses smart contracts to automatically execute transactions of AI services in a decentralized network; ⑤ Points management unit, which rewards participants who contribute data and computing power in the decentralized network through a points incentive mechanism; ⑥ Network governance unit, which uses a decentralized autonomous organization (DAO) to implement network governance and decision-making.

2. The decentralized AI service network system according to claim 1, wherein: The encryption processing unit uses homomorphic encryption and secure multi-party computing (SMC) technology to protect data from unauthorized access and still allow computational analysis.

3. The decentralized AI service network system according to claim 1, wherein: The data storage unit uses a content-addressed distributed file system, such as IPFS, to improve data durability and accessibility.

4. The decentralized AI service network system according to claim 1 or 3, wherein: The computing resource management unit includes a decentralized computing resource pool that allows users to register and contribute their computing resources, and allocates point rewards based on resource usage.

5. A method for providing AI services using a decentralized AI service network system (as claimed in claim 1), comprising the following steps: ① Receive AI service requests; ② Determine the required data and computing resources based on the request; ③ Dispatching computing resources in a decentralized network to complete AI services; ④ Use encryption technology to protect data transmission and processing; ⑤ After completing the AI ​​service, participants who contributed data and computing power will be rewarded through a points reward mechanism; ⑥ Use network governance units to conduct service quality supervision and network governance.

6. The method according to claim 5, wherein: AI services include but are not limited to data analysis, machine learning model training, image recognition, and natural language processing.

7. The method according to claim 5 further comprises using differential privacy technology to analyze the data without leaking personal privacy.

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