From "Holy Grail" to Cornerstone: How FHE Is Reshaping the Web3 Privacy Computing Ecosystem?
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From "Holy Grail" to Cornerstone: How FHE Is Reshaping the Web3 Privacy Computing Ecosystem?
FHE, as a holy grail-level encryption technology, is poised to become one of the cornerstones of security and likely to be more widely adopted, given that AI represents the future.
Navigating Web3 tides with focused insightsI've mentioned in several previous articles that AI Agents could serve as the "redemption" for many longstanding narratives in the crypto industry. During the last wave of narratives centered around AI autonomy, TEE briefly rose to prominence. However, there's another cryptographic concept even more obscure than TEE or ZKP—FHE (Fully Homomorphic Encryption)—that may also experience a rebirth, driven by the rise of AI.
FHE is a cryptographic technique that allows computations to be performed directly on encrypted data, long regarded as the "Holy Grail" of cryptography. Compared to popular technologies like ZKP and TEE, FHE remains relatively niche, primarily due to high computational overhead and limited practical use cases.
Mind Network is an infrastructure project focused specifically on FHE, and has launched MindChain—an FHE-dedicated chain tailored for AI Agents. Despite raising tens of millions in funding and years of technical development, market attention remains limited, largely constrained by the inherent challenges of FHE itself.
Recently, however, Mind Network has announced several positive developments tied to AI applications. For instance, its FHE Rust SDK has been integrated into DeepSeek, a leading open-source large model, becoming a critical component in AI training and providing a secure foundation for trustworthy AI. Why is FHE well-suited for privacy-preserving computation in AI, and can it leverage the AI Agent narrative to achieve a breakthrough—or redemption?
In simple terms: FHE (Fully Homomorphic Encryption) is a cryptographic technology that can be directly layered atop existing public blockchain architectures. It enables arbitrary computations—including addition and multiplication—on encrypted data without requiring decryption.
In other words, FHE ensures end-to-end encryption from data input to output. Even nodes responsible for validation under public blockchain consensus cannot access plaintext information. This makes FHE a strong foundational technology for training AI LLMs in sensitive vertical domains such as healthcare and finance.
FHE thus emerges as a preferred solution for expanding traditional AI large models into specialized vertical applications and integrating them with blockchain’s decentralized architecture. Whether enabling cross-institutional collaboration on medical data or privacy-preserving inference in financial transactions, FHE offers a unique complementary advantage.
This isn’t abstract—consider a concrete example: An AI Agent serving end users (C-end) typically integrates various large models from providers like DeepSeek, Claude, and OpenAI on its backend. But how can we ensure that in highly sensitive financial scenarios, the Agent’s execution isn’t suddenly altered by changes in the model provider’s backend policies? The answer lies in encrypting the input prompts. When LLM providers compute directly on ciphertext, there is no room for forced interference, preserving fairness and integrity.
Then what about the concept of "Trustworthy AI"? Trustworthy AI is the vision Mind Network aims to realize through a decentralized AI framework powered by FHE—enabling multiple parties to collaboratively train and run models efficiently using distributed GPU computing power, without reliance on centralized servers, and providing FHE-based consensus verification for AI Agents. This design overcomes the limitations of centralized AI, offering dual guarantees of privacy and autonomy for Web3 AI Agents operating within a decentralized architecture.
This aligns perfectly with Mind Network’s own narrative of a decentralized public chain. For example, during special on-chain transactions, FHE can protect the privacy of Oracle data throughout inference and execution, enabling AI Agents to make autonomous trading decisions without exposing positions or strategies.
So why do we say FHE will follow a similar industry adoption path as TEE, gaining direct opportunities from the explosion of AI applications?
TEE previously seized the opportunity presented by AI Agents because its hardware environment enables private data custody—allowing AI Agents to autonomously manage private keys and fueling the new narrative of AI self-custody of assets. However, TEE has a critical flaw: trust depends on third-party hardware vendors (e.g., Intel). To make TEE effective, a decentralized chain architecture must add an extra layer of transparent "consensus" to constrain the TEE environment. In contrast, FHE can exist natively within a decentralized chain architecture without relying on any third party.
FHE and TEE occupy similar ecological niches. Although TEE hasn't yet seen widespread adoption in Web3, it is already a mature technology in Web2. Similarly, FHE is likely to find value in both Web2 and Web3 amid this new wave of AI advancement.
In conclusion, it's clear that FHE—the holy grail of encryption technologies—will likely become one of the cornerstones of security as AI shapes the future, with growing potential for broad adoption.
Of course, we must also acknowledge the significant computational costs involved in FHE implementation. If FHE can first gain traction in Web2 AI applications, and then bridge into Web3 AI use cases, it could unexpectedly unlock economies of scale, reducing overall costs and accelerating widespread adoption.
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Haotian | CryptoInsight
