I. Necessary Conditions for the Infrastructure Layer of AgenticWorld
1. Decentralized Identity Authentication System
- Technical Requirements: Each AI agent must have a unique and non-reproducible identity identifier to prevent malicious node forgery (Sybil attack).
- FHE Function: Achieve zero-knowledge verification of identity attributes (such as age, permission level) through fully homomorphic encryption, allowing on-chain authentication without exposing raw data.
- Case Benchmark: Similar to the EU eIDAS 2.0 framework, but needs to be compatible with the dynamically evolving AI identity.
2. Data Sovereignty Protocol against Quantum Computing
- Core Contradiction: The separation of ownership and usage rights of AI training data (e.g., privacy compliance when training models with medical data).
- FHE Breakthrough Point:
- Supports direct computation of gradient descent on encrypted data (e.g., ML applications of Microsoft SEAL library);
- Saves 90% of communication costs compared to MPC (Secure Multi-Party Computation) (IBM 2023 white paper data).
3. Verifiable Computing Network
- Pain Point: The conflict between the black box nature of AI decision-making processes and the auditability of results.
- Solutions$:
- FHE+ZK-Rollup constructs a dual channel of 'encrypted computation - plaintext verification';
- Off-chain encrypted inference, generating validity proofs on-chain (reference Aleo's zkML architecture).
II. Core Value of FHE in the Collaborative Network Layer
1. Nash Equilibrium Implementation of Multi-Agent Game
- Problem: AI may fall into the prisoner's dilemma in resource competition scenarios (e.g., computation power contention).
- FHE Empowerment Mechanism:
- Encryption strategy space: Hiding the decision tree weights of agents;
- Explicit game results: Output balanced solutions through FHE computation;
- Experimental data: MIT improved the Fictitious Play algorithm using FHE in 2023, achieving a 47% increase in convergence speed.
2. Trust Minimization in Cross-Chain Collaboration
- Current Bottleneck: Cross-chain protocols like Cosmos/IBC cannot meet the millisecond-level response requirements between AIs.
- FHE Innovation Path:
- Construct homomorphic encryption state channels (HE-Payment Channel) to achieve cross-chain atomic transactions;
- Empirical comparison: Traditional cross-chain delay 800ms vs FHE cross-chain 220ms (Solana Labs test network data).
III. Rigid Constraints of the Economic System Layer
1. Privacy Asset Liquidity Protocol
- Necessity: AI computation power leasing and data trading need to comply with regulations such as GDPR/CCPA.
- FHE Implementation Path:
- Tornado Cash upgraded version: Supports ERC-721 format for model parameter trading;
- Regulatory Compatible Design: Open regulatory keys to compliance agencies (similar to Zcash's View Key).
2. Anti-MEV (Miner Extractable Value) AI Market
- Data Evidence: Current DeFi market MEV annual losses exceed $1.2B (Flashbots 2023 report).
- FHE Defense Solutions:
- Transaction intention encryption: Hiding the resource bidding strategies of agents;
- Batch settlement mechanism: Package over 100 transactions to generate a single FHE proof.
IV. Non-Negotiable Principles of Governance Structure
1. Distributed circuit breaker mechanism
- Technical Indicators: Must meet Byzantine fault tolerance threshold (≥3f+1 nodes).
- FHE Implementation Plan:
- Key sharding stored in different jurisdictions;
- Anomaly detection triggers threshold homomorphic decryption (e.g., AI network entropy changes above 0.3).
2. Dynamic Compliance Sandbox
- Regulatory Technology (RegTech) Innovation:
- FHE implements regulatory submissions of 'data available but not visible';
- Case: The UK's FCA has tested the FHE version of the TRM system.
V. Feasibility Verification for Commercial Landing
1. Hardware Layer Acceleration Progress
- Intel SGX2 optimization of the FHE instruction set, increasing RSA-2048 encryption speed by 18 times;
- Dedicated Chips: Cornami (SoftBank investment) FHE chip power consumption reduced to 1/40.
2. Cost Decline Curve
- Cost of a single FHE operation in 2023: $0.12 → Predicted $0.003 in 2025 (predicted by Zama);
- Critical Point: Erupts when costs are lower than average losses from data breaches (IBM: $4.45M/incident).
Conclusion Framework
AgenticWorld is not just a simple stack of technologies, but a complex system intersecting cryptography, game theory, and law. The core value of FHE lies in its simultaneous fulfillment of:
1. Privacy: Clear ownership of data sovereignty;
2. Compliance: Meets global regulatory frameworks;
3. Efficiency: Without sacrificing the cooperation speed of agents.
The current closest to practical scenarios is the medical AI and financial prediction market, focusing on the evolutionary path of FHE and ZK integration.
