BOINC AI White paper
  • 🌍Abstract
  • 🌍1. BOINC AI Background
    • 1.1 Introduction to BOINC AI
    • 1.2 Introduction to BOINC AI Technology
    • 1.3 Supported AI Projects
  • 🌍2. BOBchain
    • 🌍2.1 BOBchain background
      • 2.1.1 AI development is proportional to arithmetic demand
      • 2.1.2 There is a paradox of sequencing between industry landing and technology diffusion
      • 2.1.3 Uneven Distribution of Arithmetic Power, and Lack of a Firm Community Base
      • 2.1.4 Lack of Mechanisms to Achieve Economic Circularity;
    • 2.2 Overview
    • 🌍2.3 Technical Features
      • 2.3.1 L1 Layer Network
      • 2.3.2 L2 Layer 2 Network
      • 2.3.3 PoVC Arithmetic Contribution Value Consensus
      • 2.3.4 Decentralized Distributed Storage of Encrypted Data;
      • 2.3.5 Proof of Zero Knowledge
      • 2.3.6 Blockchain Standardization for AI
  • 🌍3. BOINC AI Miner
    • 3.1 Hardware Binding and Verification
      • 3.1.1 Binding the Miner to the Chain
      • 3.1.2 Zero proof of knowledge is used for miner registration
      • 3.1.3 Mining Group Network Validation
      • 3.1.4 Compatible Smart Contracts
    • 3.2 Node Client
      • 3.2.1 Initialization of the Mining System
      • 3.2.2 Miner Hardware Identifier
      • 3.2.3 Proof of zero knowledge of the miner is submitted for validation
    • 3.3 AI Training and Validation
      • 3.3.1 Data Hashing
      • 3.3.2 Random Sampling
      • 3.3.3 Validating the AI Model
  • 🌍4. Data Privacy and Security
    • 4.1 Model Confirmation and Training Data Privacy and Security
    • 4.2 Distributed Cryptographic Storage
    • 4.3 Zero-knowledge proof protects user privacy
  • 🌍5. Artificial Intelligence Ecology
    • 5.1 BOINC AI Miner Community
    • 5.2 Web3 Standards for AI Ecology
    • 5.3 Decentralised Model Rental and Trading Marketplace
    • 🌍5.4 BOINC AI Foundation
      • 5.4.1 Overview
      • 5.4.2 Strategic Decision Committee
      • 5.4.3 Committee on Technology Development
      • 5.4.4 Public Relations Committee
      • 5.4.5 Secretariat
      • 5.4.6 Token economy model
    • 5.5 BOINC AI Team
    • 5.6 BOINC AI Development Roadmap
  • 🌍6. Super AI and AI Company
    • 6.1 Super AI Backgroud
    • 6.2 What can AI company do on the platform
    • 6.3 How does AI company issue STO tokens
    • 6.4 How to invest in AI companies in the miners
Powered by GitBook
On this page
  1. 4. Data Privacy and Security

4.1 Model Confirmation and Training Data Privacy and Security

The js-ipfs library is used to store and transfer large model files, and only the content path hash is recorded on the chain to ensure privacy and data security. The hash chain ensures data integrity and consistency, and each file has a unique hash value, i.e., it provides tamper-proof model and data protection for AI training. The decentralized and tamper-proof characteristics provide a reliable solution and trustworthiness for model authentication, and model contributors can achieve the protection of their rights and interests in the model as an asset on the BOBchain chain without revealing their identities through zero-knowledge proof.

Previous4. Data Privacy and SecurityNext4.2 Distributed Cryptographic Storage

Last updated 1 year ago

🌍
Page cover image