Agentora White Paper
  • Preface
  • 1. Agentora
  • 2. Key Technologies
    • 2.1 Federated Learning Framework and AI Agents
    • 2.2 ZKML Verification Protocol
    • 2.3 Dynamic Game Engine
    • 2.4 Triple Trust Verification System
    • 2.5 Tokenized Computational Power Market and AI Training Tax Mechanism
  • 3. Web3 AI Economy
    • 3.1 Autonomous Universe of GameFi
    • 3.2 AI Agent Native Driver
    • 3.3 Self-earning Capability
  • 4. Tokenomics
    • 4.1 $ATA (Agentora Token)
    • 4.2 ATA Application and Circulation
  • 5. Roadmap
  • 6. Investment and Project Risk Information
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  1. 2. Key Technologies

2.2 ZKML Verification Protocol

Zero-Knowledge Machine Learning (ZKML)

  • Concept and Implementation:The ZKML protocol applies the principles of zero-knowledge proofs to machine learning models, ensuring that the correctness of model execution can be verified without revealing the internal data and parameters of the model. Agentora has invested $1.32 million in reconstructing this protocol to ensure its security and efficiency.

  • Privacy and Security:During the training process, decisions and parameter updates of AI models are verified through zero-knowledge proofs. This ensures that external observers cannot steal sensitive information, while still confirming that the decision-making process complies with predetermined rules.

  • On-Chain Verification:Each AI decision is accompanied by an immutable model fingerprint that is verified on-chain through the ZKML protocol. This ensures that all decision-making processes are transparent, traceable, and tamper-proof.

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Last updated 3 months ago