Mind Network is building a new infrastructure for privacy computing and decentralized collaboration in the AI field through Fully Homomorphic Encryption (FHE) technology, reshaping the future of AI on multiple levels through its technical applications and ecological layout. Key points analysis is as follows:
I. Technical advantages of FHE: From 'Holy Grail' to cornerstone of AI privacy computing
Fully Homomorphic Encryption (FHE) allows for direct computation on encrypted data (such as addition and multiplication) without the need to decrypt, enabling the handling of sensitive information and is regarded as the 'Holy Grail' of privacy computing. Compared to other technologies (like ZKP, TEE), the core advantage of FHE is:
1. End-to-end data encryption: The entire computation process from input to output is completed in ciphertext, preventing nodes from accessing plaintext information, suitable for training and inference of AI models in highly sensitive scenarios like healthcare and finance.
2. Decentralized compatibility: FHE does not rely on hardware vendors (unlike TEE which needs Intel support) and can be directly integrated into blockchain architectures, providing dual guarantees of autonomous decision-making and privacy protection for Web3 AI Agents.
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II. Mind Network's FHE ecological layout
1. AgenticWorld: Trustworthy AI collaboration ecosystem
- Mind Network will launch AgenticWorld on BNBChain on April 1, 2025, building four security pillars for agent-based AI (Agentic AI): consensus security, data security, computation security, and communication security. This ecosystem supports the creation, training, and collaboration of agents and plans to expand to multi-chain networks (like MindChain), having already protected the operation of over 3,000 agents.
- Application scenarios: Cross-institution collaboration on medical data, encrypted inference, multi-agent systems (MAS), etc., for example, protecting the privacy of AI Agent's position strategy and trading decision through FHE.
2. Technical cooperation and integration
- Collaboration with Phala Network: Combining FHE and TEE technologies, Phala provides a low-cost hardware-level trusted computing environment, while Mind Network is responsible for encrypted data verification, forming end-to-end secure services that enhance the computational efficiency and privacy of AI Agents.
- Launching ASI Hub with SingularityNET: Based on FHE to solve AI Agent identity verification and verifiable randomness issues, enhancing the anti-tampering capabilities of AI services, and incentivizing ecological participation through $vFHE points.
3. Developer tools and open-source ecosystem
- FHE Rust SDK: Already integrated into the open-source large model DeepSeek, providing underlying security guarantees for AI training and promoting the realization of 'trusted AI'.
- Ecological expansion: Over 2 million addresses covered through the CitizenZ Passport airdrop, accelerating participation from users and developers.
III. Industry impact and challenges of FHE+AI
1. Solve the core pain points of AI
- Data privacy: FHE ensures the encryption of training data and inference processes, preventing model black boxes and data leaks, such as protecting user prompts from tampering in financial scenarios.
- Decentralized autonomy: Eliminating reliance on centralized servers through distributed GPU computing power and on-chain consensus, granting AI Agents greater autonomy and transparency.
2. Technical challenges and pathways to scaling
- Computational overhead: The complexity of FHE operations is high, and cost remains a bottleneck. Mind Network proposes to leverage the synergy between Web2 and Web3 scenarios (such as the integration of healthcare and blockchain) to dilute costs through economies of scale.
- Ecological competition: TEE technology is widely used in Web2 due to its hardware maturity, while FHE needs to further prove its practicality in complex scenarios, such as integration with quantum-resistant protocols (e.g., Mind Network's HTTPZ protocol).
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IV. Future outlook: The Web3 paradigm of AI Agents
Mind Network's vision is to promote Web3 into the 'Agentic Era', and its technical path may trigger the following trends:
1. Cross-chain AI collaboration: Achieving secure interactions and resource sharing among agents through multi-chain networks (like BNBChain, MindChain).
2. Compliance and commercial implementation: In highly regulated fields like healthcare and finance, FHE may become the standard technical option for compliant AI services.
3. Technology integration: Combining FHE with Zero-Knowledge Proofs (ZKP), federated learning, etc., to construct a multi-layer privacy computing framework.
Summary
Mind Network has redefined the boundaries of AI privacy computing through FHE technology. Its ecological layout not only addresses the core issues of data security and decentralized autonomy but also provides a feasible path for the large-scale application of AI Agents. Despite facing challenges in computational costs and ecological adaptation, its explorations in the synergy of Web3 and Web2, as well as cross-domain collaboration, may become a key engine driving the future development of AI.
