From "Holy Grail" to Cornerstone: How Does FHE Reshape the Web3 Privacy Computing Ecosystem?

Author: Chain View

I have said in many articles before that AI Agent will be the "redemption" of many old narratives in the Crypto industry. In the last wave of narrative evolution around AI autonomy, TEE was once on the cusp of the storm. However, there is another technical concept that is more "unpopular" than TEE and even ZKP, FHE - fully homomorphic encryption, which will also be "reborn" due to the promotion of the AI ​​track. Below, I will sort out the logic for you through examples:

FHE is a cryptographic technology that allows direct calculations on encrypted data. It is regarded as a "Holy Grail". Compared with popular technical narratives such as ZKP and TEE, it is in a relatively unpopular position. The core is mainly constrained by overhead and application scenarios.

Mind Network is focusing on the infrastructure of FHE and has launched the FHE Chain, MindChain, which focuses on AI Agent. Although it has raised more than 10 million US dollars and has experienced several years of technical cultivation, it is still underestimated by the market due to the limitations of FHE itself.

However, Mind Network has recently released a lot of good news around AI application scenarios. For example, the FHE Rust SDK developed by it has been integrated by the open source large model DeepSeek, becoming a key link in the AI ​​training scenario, providing a secure foundation for the implementation of trusted AI. Why can FHE be used in AI privacy computing? Can it achieve a curve overtaking or redemption with the help of AI Agent's narrative?

In short: FHE fully homomorphic encryption is a cryptographic technology that can be directly applied to the current public chain architecture, allowing arbitrary calculations such as addition and multiplication to be performed directly on encrypted data without decrypting the data first.

In other words, the application of FHE technology can encrypt data from input to output. Even nodes that maintain the public chain consensus for verification cannot access plaintext information. In this way, FHE can provide technical bottom-level protection for the training of some AI LLM in vertical segmentation scenarios such as medical and financial.

FHE can become a "preferred" solution for traditional AI large model training to enrich and expand vertical scenarios and combine blockchain distributed architecture. Whether it is cross-institutional collaboration of medical data or privacy reasoning in financial transaction scenarios, FHE can become a supplementary choice with its uniqueness.

This is actually not abstract. It can be understood with a simple example: For example, AI Agent, as a C-end application, usually has access to AI large models provided by different suppliers including DeepSeek, Claude, OpenAI, etc., but how to ensure that in some highly sensitive financial application scenarios, the execution process of AI Agent will not be affected by the large model background that suddenly tampers with the rules? This will inevitably require the input prompt to be encrypted. When the LLMs service provider directly calculates and processes the ciphertext, there will be no forced interference and modification that affects fairness.

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So what about the other concept of "trusted AI"? Trusted AI is a FHE decentralized AI vision that Mind Network is trying to build, including allowing multiple parties to achieve efficient model training and reasoning through distributed computing power GPUs without relying on central servers, and providing AI Agents with FHE-based consensus verification. This design eliminates the limitations of the original centralized AI and provides privacy + autonomy dual protection for the operation of web3 AI Agents 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 a special on-chain transaction process, FHE can protect the privacy reasoning and execution process of the Oracle data of all parties, allowing AI Agents to make autonomous decisions on transactions without exposing positions or strategies, etc.

Then, 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?

Previously, TEE was able to seize the opportunity of AI Agent thanks to the TEE hardware environment, which can realize data hosting in a private state, and then allow AI Agent to independently host private keys, allowing AI Agent to achieve a new narrative of autonomous asset management. However, there is actually a flaw in TEE's custody of private keys: trust must rely on third-party hardware providers (such as Intel). In order for TEE to work, a distributed chain architecture is needed to add an additional set of open and transparent "consensus" constraints to the TEEs environment. In contrast, PHE can exist entirely based on a decentralized chain architecture without relying on a third party.

FHE and TEE have similar ecological niches. Although TEE is not widely used in 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 outbreak of this round of AI trends.

That’s all.

To sum up, it can be seen that FHE, an encryption technology at the holy grail level of encryption, will inevitably become one of the cornerstones of security and be further widely adopted under the premise that AI becomes the future.

Of course, despite this, the cost of FHE’s algorithm implementation cannot be avoided. If it can be applied in the web2 AI scenario and then linked to the web3 AI scenario, it will surely release the “scale effect” unexpectedly and dilute the overall cost, allowing it to be more widely used.