From the "Holy Grail" to the cornerstone: How does FHE reshape the Web3 privacy computing ecosystem?

I have said in many articles that AI Agent will be the "redemption" of many old narratives in the Crypto industry. In the last wave of narrative evolution around AI independence, TEE was once praised as the top spot. However, there is a technical concept FHE that is more "unpopular" than TEE and even ZKP -full homomorphic encryption, which will also be "rebirth" due to the driving force of the AI ​​track. Here is a summary of the logic for you through the case:

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

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

However, Mind Network has recently launched a lot of good news around AI application scenarios. For example, the FHE Rust SDK it developed is integrated by the open source big model DeepSeek, becoming a key link in the AI ​​training scenario, providing a security foundation for the implementation of trusted AI. Why can FHE be expressed in AI privacy computing? Can it be achieved through the narrative of AI Agent?

Simply put: FHE fully homomorphic encryption is a cryptographic technology that can directly act on the current public chain architecture, allowing for arbitrary calculations such as addition and multiplication on encrypted data without the need to decrypt the data first.

In other words, the application of FHE technology can enable full encryption of data from input to output, and even nodes that maintain public chain consensus for verification cannot access plain text information. In this way, FHE can provide technical underlying guarantees for the training of some AI LLMs in vertical segmentation scenarios such as medical care and finance.

Let FHE become a "preferred" solution for traditional AI big 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 complementary option with its uniqueness.

This is actually not abstract. You can understand it with a simple example: For example, as a C-end application, the background of the AI ​​Agent is usually connected to AI big models provided by different suppliers such as DeepSeek, Claude, OpenAI, etc., but how to ensure that in some highly sensitive financial application scenarios, the execution process of the AI ​​Agent will not be affected by the background of the big model that suddenly tampers with rules? This requires the input Prompt to be encrypted. When the LLMs service provider directly calculates the ciphertext, there will be no forced interference and changes that affect fairness.

So what's going on with 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 inference through distributed computing power GPUs, without having to rely on a central server, and providing FHE-based consensus verification for AI Agents. This design eliminates the limitations of originally centralized AI and provides dual guarantees of privacy + autonomy for the operation of web3 AI Agent 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 special on-chain transactions, FHE can protect the privacy reasoning and execution of Oracle data of all parties, allowing AI Agents to make independent decisions on transactions without exposing positions or strategies, etc.

So, why does FHE have similar industry penetration paths like TEE, which will bring direct opportunities due to the outbreak of AI application scenarios?

Previously, TEE was able to seize the opportunity of AI Agent because the TEE hardware environment could enable data to be hosted in a private state, and then allow AI Agent to independently host private keys, allowing AI Agent to reach a new narrative of independent management of assets. However, TEE actually has a flaw in keeping private keys: trust depends on third-party hardware providers (such as Intel). To make TEE work, a distributed chain architecture is needed to add an additional open and transparent "consensus" constraint to the TEEs environment. In contrast, PHE can exist entirely 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 has long been a very mature technology in the web2 field. In contrast, FHE will gradually find value in both web2 and web3 under the explosion of this round of AI trends.

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

Of course, despite this, it is necessary to avoid the overhead cost issue of FHE when implementing the algorithm. If it can be applied in the web2 AI scenario and then linked to the web3 AI scenario, it will probably unexpectedly release the "scale effect" and dilute the overall cost, allowing it to be more popular.