1. Analysis of FHE Use Cases and Key Landing Scenarios in the AI Field
1. Medical Field
- Cross-organization data collaboration: FHE allows medical institutions to jointly train AI models in an encrypted state, for example, encrypted medical data from different hospitals can be directly used to analyze rare disease characteristics without sharing plaintext data, breaking down data silos.
Patient privacy protection: In AIaaS (AI as a Service) scenarios, patients' genomic data or medical records can be uploaded to the cloud for diagnostic analysis after being encrypted by FHE, with only the user holding the decryption key, ensuring confidentiality throughout the data lifecycle.
2. AI Model Training and Inference
Collaborative learning and data monetization: Enterprises can train AI models on encrypted data using FHE, allowing data owners to earn profits without exposing raw data, promoting open-source collaboration.
End-to-end private inference: Users' encrypted input data (such as financial risk assessment requests) is directly processed by cloud AI and returns encrypted results, preventing model providers from stealing sensitive information.
2. How FHE Provides Core Support for AI Agents
1. Identity Verification and Tamper Resistance
Proof of Human: Applications like Privasea's ImHuman use FHE to encrypt biometric features (such as facial recognition) to generate a unique NFT proving the user is a real person, preventing AI impersonation.
Decentralized Identity (DID): The identity credentials of agents can be stored encrypted on-chain, verifying their legitimacy through FHE without exposing details.
2. Secure Computing Environment
End-to-end encryption: The interaction data of agents is encrypted throughout the process, even if nodes validate consensus, plaintext cannot be obtained, ensuring privacy of strategies and transactions (e.g., Mind Network's FHE Chain).
Verifiable computation: Combined with ZKP, FHE allows third parties to verify the correctness of computations, preventing malicious agents from fabricating results.
3. Necessary Conditions for AgenticWorld and the Core Role of FHE
1. Necessary Conditions
Privacy and Security Underlying Protocols: Full-process encryption of data from input to output to prevent agents from leaking user privacy.
- Economic incentive system: Reward participating agents in computation with tokens (such as $FHE) to form a sustainable ecosystem.
4. The Value of FHE in the Integration of AI and Blockchain
1. Multi-chain Collaboration and Cross-chain Privacy
Encrypted State Sharing: FHE allows secure interaction of encrypted data across different chains (such as Ethereum and Cosmos), supporting cross-chain DeFi and AI model aggregation.
