This article allows us to delve into how the Mind Network project utilizes Fully Homomorphic Encryption (FHE) technology to build a secure and trustworthy infrastructure for AI agents, especially in multi-agent collaboration scenarios to address core issues such as data privacy, consensus security, and computational transparency. Below is a brief summary of the article:
1. Background and Core Issues
AI agents are a hot topic in the field of artificial intelligence and are considered the ‘next-generation robotics industry’ with huge potential. However, as agent capabilities increase, issues such as data privacy, computational security, and collaborative trust become increasingly prominent. Traditional encryption technologies (such as computing after decryption) struggle to meet these needs, while FHE, as a cryptographic technology that allows computation without decryption, provides new possibilities for the secure collaboration of AI agents.
2. Overview of Mind Network Project
Establishment and Vision: Mind Network was founded in 2022, aiming to build a ‘trusted operating system’ through FHE technology, supporting AI agents to operate autonomously under privacy protection, addressing four major security challenges: consensus, data, computation, and communication.
Funding and Cooperation: The project received $12.5 million in funding, supported by institutions such as Binance Labs and Animoca Brands, selected for the Binance Labs incubation program, and collaborated with Zama, Chainlink, and others.
Technological Innovation: Launching the HTTPZ protocol (a zero-trust network protocol based on FHE), achieving data encryption throughout its lifecycle; the mainnet is scheduled to launch in November 2024, with the token generation event (TGE) completed by 2025.
3. Core Advantages of FHE
FHE allows direct computation on encrypted data, with results remaining encrypted, eliminating the need for decryption to complete data processing. Compared to Zero-Knowledge Proofs (ZK) and Multi-Party Computation (MPC), FHE is more suitable in AI scenarios for handling sensitive data, ensuring privacy and security. Its application scenarios include healthcare, finance, and the Internet of Things.
4. Challenges of AI Agents and Solutions from Mind Network
Multi-Agent Collaboration Challenges: Limited capabilities of a single agent, multi-agent systems face challenges in consensus consistency, data privacy, and decision-making efficiency. For example, in autonomous driving, different agents need to quickly reach a consensus on emergency braking; in healthcare scenarios, patient data privacy needs to be protected.
Mind Network's Solution: Data Sovereignty: Achieving end-to-end encryption of data through FHE to prevent leaks.
Fair Consensus: FHE-based encrypted verification improves consensus efficiency and credibility.
Communication Security: Supports encrypted interaction in a zero-trust environment, facilitating cross-chain or cross-organization collaboration.
Trusted AI: Ensuring encryption during model training and inference processes, enhancing AI transparency and security.
5. Practical Application Cases
io.net: Integrating FHE to enhance the security of distributed computing platforms.
Chainlink: Enhancing Privacy Protection of Cross-Chain Interoperability Protocol (CCIP) through FHE.
Phala Network: Combining FHE with Trusted Execution Environment (TEE) to achieve zero-trust AI solutions.
Swarms: Optimizing multi-agent collaboration, applicable in fields such as financial analysis and medical diagnosis.
6. Advantages and Challenges
Advantages: Pioneering the application of FHE in AI consensus management.
End-to-end encryption enhances data security, suitable for sensitive scenarios such as finance and healthcare.
Efficient consensus mechanism supports large-scale agent collaboration.
Flexible architecture and incentive mechanisms promote ecological expansion.
Challenge: High computational complexity of FHE, performance needs optimization.
The market has insufficient awareness of FHE, making promotion difficult.
Ecological construction still needs improvement, and application scenarios need to be expanded.
7. AgenticWorld and $FHE Token
AgenticWorld: A multi-agent ecosystem launched by Mind Network, supports users in activating and training agents through staking $FHE tokens. Agents can learn and execute tasks in basic and advanced Hubs, with expected annualized returns as high as 400%.
$FHE Token: Total supply of 1 billion, initial circulation of 24.9%. Uses include staking, task rewards, ecological governance, etc. TGE was oversubscribed 174 times, with high community participation, and token distribution emphasizes community priority.
MindChain: The world's first dedicated FHE blockchain for AI agents, supporting cross-chain collaboration and efficient computation.
8. Future Outlook
Mind Network provides a secure foundation for AI agents through FHE technology, breaking the boundaries between Web2 and Web3, empowering scenarios in healthcare, finance, DeFi, and more. The AgenticWorld ecosystem supports the autonomous growth and collaboration of agents, and will launch more advanced Hubs (such as DeepSeek Hub) and cross-chain tools in the future, combining MindDAO for community governance. Despite the performance bottlenecks faced by FHE technology, its quantum-resistant security and privacy protection potential are enormous, and Mind Network is expected to become a core infrastructure in the field of AI security.
Summary
Mind Network, centered on FHE, has built a full-stack solution addressing the security pain points of AI agents, from data protection to consensus verification. Its AgenticWorld ecosystem incentivizes through the $FHE token, connecting developers and users, and promoting the practical application of multi-agent collaboration. The project shows potential in technological innovation and ecological construction but needs to overcome performance and market awareness challenges and may play a key role in AI security and decentralized intelligence in the future.
Source: Binance Square Issue 25 Square Essay Project