From "Holy Grail" to Cornerstone: How FHE is Reshaping the Web3 Privacy Computing Ecosystem?

I have previously mentioned in several articles that AI Agent will be the "redemption" of many old narratives in the Crypto industry. In the last wave of narrative evolution surrounding AI autonomy, TEE was once elevated to the forefront, but there is another even more "niche" technological concept than TEE and even ZKP, which is FHE—fully homomorphic encryption, that will also gain "rebirth" due to the AI sector. Below, I will outline the logic through examples:

FHE is a cryptographic technique that allows computations to be performed directly on encrypted data, regarded as the "Holy Grail". Compared to popular technologies such as ZKP and TEE, it occupies a relatively niche position, primarily constrained by overhead and application scenarios.

Mind Network focuses on the infrastructure of FHE and has launched the FHE Chain, MindChain, which is dedicated to AI Agents. Despite having raised over ten million dollars and having undergone several years of technological development, its market attention remains underestimated due to the limitations of FHE itself.

However, recently Mind Network has released a number of positive news surrounding AI application scenarios. For example, its developed FHE Rust SDK has been integrated by the open-source large model DeepSeek, becoming a key component in AI training scenarios and providing a secure foundation for the realization of trustworthy AI. Why can FHE perform in AI privacy computing, and can it achieve a breakthrough or redemption through the narrative of AI Agents?

In simple terms: FHE fully homomorphic encryption is a cryptographic technology that can directly operate on the current public chain architecture, allowing arbitrary computations such as addition and multiplication to be performed directly on encrypted data without the need to decrypt the data first.

In other words, the application of FHE technology enables data to be fully encrypted from input to output, so that even nodes verifying consensus on the public chain cannot access plaintext information. This means that FHE can provide a technical foundation for training some AI LLMs in vertical segments such as healthcare and finance.

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

This is actually not abstract and can be understood with a simple example: for instance, an AI Agent as an application aimed at the C-end, its backend usually connects to different AI large models provided by suppliers such as DeepSeek, Claude, OpenAI, etc. But how can we ensure that in some highly sensitive financial application scenarios, the execution process of the AI Agent will not be suddenly affected by a large model backend that changes the rules? This will inevitably require encrypting the input Prompt, so when LLMs service providers directly perform computations on the ciphertext, there will be no forced interference that affects fairness.

So what is the concept of "trustworthy AI"? Trustworthy AI is a vision of decentralized AI that Mind Network attempts to build, which includes allowing multiple parties to achieve efficient model training and inference through distributed computing power (GPU) without relying on a central server, providing FHE-based consensus verification for AI Agents, etc. This design eliminates the limitations of traditionally centralized AI, offering dual guarantees of privacy and autonomy for web3 AI Agents operating under a distributed architecture.

This is more in line with the narrative direction of Mind Network's own distributed public chain architecture. For example, in the special on-chain transaction process, FHE can protect the privacy inference and execution process of Oracle data of all parties, and enable AI agents to make independent decisions on transactions without exposing positions or strategies.

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

The previous opportunity for TEE to capture the AI Agent was due to the fact that the TEE hardware environment can host data in a state of privacy, allowing the AI Agent to autonomously manage private keys, thus achieving a new narrative of autonomous asset management. However, there is a significant flaw in TEE's management of private keys: trust relies on third-party hardware providers (such as Intel). In order for TEE to function effectively, a distributed chain architecture is needed to impose an additional public and transparent "consensus" constraint on the TEE environment. In contrast, PHE can completely exist based on a decentralized chain architecture without relying on third parties.

FHE and TEE have similar ecological niches; although TEE is not widely used in the web3 ecosystem, it is already a very mature technology in the web2 field. In comparison, FHE will gradually find its value in both web2 and web3 during this wave of AI trend explosion.

Above.

In summary, it can be seen that FHE, this encryption holy grail-level encryption technology, will inevitably become one of the cornerstones of security under the premise that AI becomes the future, and there is a possibility of being further widely adopted.

Of course, despite this, the cost issue of FHE in algorithm implementation 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 the overall cost, allowing for more widespread application.