In 2025, one of the hottest wealth codes in the crypto world is undoubtedly the airdrop of Mind Network's CitizenZ Passport.
In just three months, this ecological certificate has covered over 2 million addresses, with the number of mainnet wallets exceeding 210,000 and average daily interactions exceeding 1.2 million.
Behind the airdrop is Mind Network, centered on fully homomorphic encryption (FHE), collaborating with giants like ZAMA and InfStones to build the 'AI agent collaboration ecosystem'.
This airdrop is not only a wealth effect but also signifies a new paradigm of deep integration between AI and blockchain — the end-to-end encrypted Agentic World is rising.
One, AI + blockchain: A paradigm revolution from 'data islands' to 'intelligent collaboration'.
Traditional AI is constrained by centralized monopolies and data privacy conflicts, while the transparency of blockchain fails to meet privacy demands. The combination of the two, through decentralized AI agents and multi-chain collaborative architecture, is breaking the deadlock:
1. Multi-chain compatibility: Mind Network launched AgenticWorld based on the BNB Chain and plans to expand to MindChain, Ethereum, and other networks to build a cross-chain agent collaboration layer.
2. Trusted execution environment: Blockchain provides identity binding and on-chain verification for AI agents, such as AI oracles ensuring decision transparency through multi-node consensus.
3. Economic incentive model: Users stake $FHE tokens to activate AI agents, earning rewards through training and cooperation, forming a closed-loop ecosystem.
Pain point breakthrough: Traditional AI relies on centralized servers, leading to blurred data ownership; the transparency of blockchain results in privacy leakage risks. The combination of the two needs to balance privacy and efficiency, and FHE is the key.
Two, FHE: The 'holy grail technology' of AI security infrastructure.Fully homomorphic encryption (FHE) allows direct computation on encrypted data without decryption, completely solving the privacy problem of combining AI and blockchain. Its core value is reflected in three major scenarios:
1. Data sovereignty protection: Sensitive data in healthcare, finance, etc., can be encrypted with FHE before training AI models, avoiding the risks of monopoly and leakage by giants. For example, Mind Network and DeepSeek collaborate to achieve encrypted medical data analysis.
2. Multi-agent cooperative security: FHE ensures that the data transmitted between Agents is encrypted throughout the process, making it impossible for malicious nodes to glimpse the original information, thus cracking the hidden dangers of the 'black box model'.
3. On-chain privacy computation: When smart contracts invoke AI services, FHE can protect input parameters and output results, avoiding MEV attacks and strategy exposure.
Comparison of ZK and TEE: Zero-knowledge proofs (ZK) rely on third parties to generate proofs, multi-party computation (MPC) has high communication costs, while FHE's 'end-to-end encryption' feature is more suitable for AI's linear computing structure, becoming the ultimate solution for privacy computation.
Three, Agent consensus mechanism: How does FHE crack the 'Westworld-style defection'?
The AI awakening parable of (Westworld) reveals the core risks of multi-agent systems — malicious cooperation and data tampering. FHE builds a foundation of trust through four major security pillars:
1. Consensus security: The encryption verification mechanism ensures that Agent behavior is traceable, such as Mind Network's FHE voting system MindV.
2. Data security: Sensitive data such as health and finance is always encrypted, making it impossible for cloud service providers to peek.
3. Computational security: FHE supports model inference in an encrypted state, making the process auditable and avoiding 'black box operations'.
4. Communication security: Zero trust protocols prevent transmission links from being hijacked, resisting witch attacks and man-in-the-middle intrusions.
Case study: In autonomous driving, perception, decision-making, and control agents need to cooperate in real-time. FHE can encrypt the data stream of each module, ensuring that malicious nodes cannot tamper with instructions.
Four, why is 'end-to-end encryption' a must-have for the future of AI?
Traditional HTTPS only encrypts at the transport layer, requiring decryption during data processing, which poses exposure risks. The HTTPZ protocol driven by FHE achieves full lifecycle encryption:
Encryption scope: Data is encrypted throughout transmission, storage, and computation, making it impossible for cloud service providers and hackers to crack it.
Zero trust architecture: Does not rely on any intermediaries, each request must be independently verified, suitable for decentralized scenarios.
Quantum resistance: The FHE algorithm resists quantum computing attacks, providing a long-term security foundation for AI + blockchain.
Industry trend: Vitalik Buterin recently published an article supporting FHE, stating that its integration with AI is 'the core solution to privacy issues'. Giants like Microsoft and Google are also accelerating their layouts; the large-scale application of FHE will reduce computing costs and promote technology popularization.
Conclusion: FHE + AI — the 'invisible engine' of the next bull market.
From airdrop frenzy to technological implementation, FHE is transforming from the 'cryptographic holy grail' to the security cornerstone of the AI era. The TGE of Mind Network raised 174 times the target, and the CitizenZ Passport has a million-level coverage, confirming the market's urgent demand for 'privacy-first AI'.
In the future, whoever can master the end-to-end encrypted ecosystem of intelligent cooperation will be able to seize the high ground in the next trillion-dollar track amid the wave of AI and blockchain integration.
