How FHE Reshapes the Web3 Privacy Computing Ecosystem?

#MindNetwork全同态加密FHE重塑AI未来 FHE is a cryptographic technology that allows calculations to be performed directly on encrypted data, regarded as the 'Holy Grail'. Compared to popular technologies like ZKP and TEE, it is relatively less known, mainly constrained by costs and application scenarios.

Mind Network focuses on FHE infrastructure and has launched an FHE Chain—MindChain, dedicated to AI Agents. Although it has raised over ten million dollars and has undergone years of technical cultivation, market attention remains underestimated due to the limitations of FHE itself.

However, recently, Mind Network has launched several favorable news regarding AI application scenarios. For instance, its developed FHE Rust SDK has been integrated with the open-source large model DeepSeek, becoming a key component in AI training scenarios and providing a secure foundation for trusted AI. Why can FHE perform in AI privacy computing, and can it leverage the narrative of AI Agents for a breakthrough or redemption?

In simple terms: FHE fully homomorphic encryption is a cryptographic technology that directly operates on the existing public chain architecture, allowing arbitrary calculations such as addition and multiplication to be performed on encrypted data without prior decryption.

In other words, the application of FHE technology can achieve full encryption from input to output, so that even the nodes validating public chain consensus cannot access plaintext information. This makes FHE capable of providing technical underpinning for AI LLM training in vertical segments such as healthcare and finance.

This allows FHE to become a 'preferred' solution for rich expansion of traditional AI large model training in vertical scenarios, as well as for collaboration with blockchain distributed architecture. Whether for cross-institutional collaboration on medical data or privacy reasoning in financial transaction scenarios, FHE can serve as a complementary option due to its uniqueness.

This is not abstract at all; a simple example clarifies it: for instance, if an AI Agent is an application aimed at the C-end, its backend typically connects to various AI large models provided by different vendors, including DeepSeek, Claude, and OpenAI. But how can we ensure that in some highly sensitive financial application scenarios, the execution process of the AI Agent is not unexpectedly influenced by a large model backend that suddenly alters the rules? This undoubtedly requires encrypting the input prompt, so that when LLM service providers directly compute on the ciphertext, there will be no forced interference altering the fairness.

So what about the other concept of 'trusted AI'? Trusted AI is a decentralized AI vision that Mind Network attempts to build with FHE, allowing multiple parties to achieve efficient model training and reasoning through distributed computing power (GPU), without relying on central servers, providing consensus verification based on FHE for AI Agents. This design eliminates the limitations of centralized AI and provides dual guarantees of privacy and autonomy for web3 AI Agents operating under a distributed architecture.

This aligns more closely with the narrative direction of Mind Network's distributed public chain architecture. For example, in special on-chain transaction processes, FHE can protect the privacy reasoning and execution processes of various Oracle data, allowing AI Agents to make autonomous trading decisions without exposing positions or strategies.

So, why do we say that FHE will have a similar industry penetration path as TEE and will bring direct opportunities due to the explosion of AI application scenarios?

Previously, TEE was able to seize the opportunity of AI Agents due to the TEE hardware environment's ability to host data in a privacy-preserving state, allowing AI Agents to autonomously manage private keys and achieve a new narrative of autonomous asset management. However, there is a fundamental flaw in TEE's management of private keys: trust relies on third-party hardware providers (e.g., Intel). For TEE to function, a set of distributed chain architecture is needed to add an additional public and transparent 'consensus' constraint to the TEE environment. In contrast, PHE can exist entirely based on a decentralized chain architecture without relying on third parties.

FHE and TEE have similar ecological positions. Although TEE is not widely applied within the web3 ecosystem, it has long been a very mature technology in the web2 field. In contrast, FHE will gradually find its value in both web2 and web3 under the current AI trend explosion.

That's all.

In summary, it can be seen that FHE, as a 'Holy Grail' level encryption technology, will inevitably become one of the cornerstones of security under the premise of AI becoming the future, with increasing possibilities for widespread adoption.

Of course, despite this, the issue of cost overhead in the algorithm implementation of FHE cannot be avoided. If it can be applied in web2 AI scenarios and then linked to web3 AI scenarios, it is likely to unexpectedly release a 'scalability effect' that dilutes overall costs, allowing for more widespread application.