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The privacy AI network technology stack is a multi-level, complex, and orderly ecosystem aimed at protecting data privacy while promoting innovation and application of AI technology. From the bottom to the top, this technology stack covers multiple key levels, each carrying specific functions and roles.
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Consensus layer
Function: The consensus layer is the bottom layer of the privacy AI network technology stack, responsible for ensuring that all nodes in the network can reach consensus on transactions and data states. This is the core part of blockchain technology, which is crucial for maintaining the security and reliability of the network.
Consensus algorithms, such as Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS), ensure that nodes in the network can honestly participate in the consensus process through different mechanisms.
Smart Contract: A smart contract is a computer program that automatically executes contract terms. It runs on the blockchain and ensures that the execution of the contract is not interfered with or tampered with by any party.
Platform examples: Blockchain platforms such as Ethereum and Polkadot provide rich smart contract functions and efficient consensus mechanisms, providing a solid foundation for privacy AI networks. -
P2P layer
Function: The P2P layer is responsible for peer-to-peer communication between nodes, allowing data and information to flow freely in the network. This is the key to achieving decentralization and distributed computing.P2P network protocols, such as BitTorrent, IPFS, etc., allow nodes to directly exchange data without going through centralized servers.
Data encryption and transmission: In P2P networks, data is typically encrypted to ensure security during transmission, while efficient transmission protocols are used to reduce latency and bandwidth consumption.
Application scenarios: P2P technology is widely used in fields such as file sharing, instant messaging, and distributed storage. -
Privacy computing layer
Function: The privacy computing layer is the core of the privacy AI network technology stack, which allows for computation and analysis while protecting data privacy. This is particularly important for handling sensitive data.
Multi party secure computing (MPC): MPC allows multiple parties to perform calculations together without exposing their respective data, thereby protecting data privacy.
Homomorphic encryption: Homomorphic encryption allows computation of encrypted data, resulting in decrypted data that is identical to the computation of plaintext data, enabling data analysis without exposing the original data.
Federated learning: Federated learning allows multiple participants to train models locally and upload model parameters or gradients to a central server for aggregation, thereby improving model performance without sharing raw data.
Platform examples: OpenMined, Algorand, and other platforms provide rich privacy computing tools and algorithms, supporting the application of multi-party secure computing, homomorphic encryption, and federated learning technologies. -
Data sharing layer
Function: The data sharing layer focuses on data generation, annotation, and sharing, providing rich data sources for the development of AI technology. At the same time, it is also responsible for ensuring the privacy and security of data during the sharing process.
Differential privacy: Protecting individual privacy by adding random noise to the data while maintaining the statistical properties of the data unchanged.
Data anonymization: By processing sensitive data (such as de identification, anonymization, etc.), the risk of data leakage is reduced.
Data market: Establish a data trading platform that allows data providers and demanders to conduct data transactions while complying with privacy protection principles.
Application scenarios: Financial risk control, healthcare, smart cities and other fields require the processing of large amounts of sensitive data. The technology of data sharing layer provides secure and efficient data processing solutions for these fields. -
Second layer privacy computing network
Function: The second layer privacy computing network is a privacy computing platform built on top of the existing blockchain network, which utilizes the transparency and immutability of blockchain to ensure the fairness and security of the privacy computing process.
State channel: Allow the creation of private channels on the blockchain, allowing both parties to conduct transactions without disclosing transaction details.
Zero knowledge proof: allows the prover to prove the truth of a proposition to the verifier without revealing the specific content or proof process of the proposition.
Platform examples: Truebit, Optimism, zkSync and other platforms provide a second layer privacy computing network based on blockchain, supporting more complex privacy computing scenarios. -
AI network layer
Function: The AI network layer focuses on the AI service market and AI agent authentication, providing a platform and support for the commercial application of AI technology. It allows users to easily access AI services and ensures the security and reliability of the services.
AI service market: Establish an online market platform that allows AI service providers and demanders to conduct transactions. The platform can provide functions such as search, matching, and evaluation for services.
Authentication and Authorization: Provide authentication and authorization mechanisms for AI agents to ensure that only authorized agents can access sensitive data or perform critical operations.
Application scenarios: Intelligent customer service, autonomous driving, intelligent healthcare and other fields require a large amount of AI service support, and the technology of AI network layer provides convenient and efficient solutions for these fields.