I have previously mentioned in several articles that AI Agents will be the "redemption" of many old narratives in the Crypto industry. During the last wave of narrative evolution around AI autonomy, TEE was once placed at the forefront. However, there is another even more "niche" technical concept than TEE and even ZKP: FHE—fully homomorphic encryption, which will also gain "rebirth" due to the drive of the AI track. Below, I will clarify the logic through examples:
FHE is a cryptographic technology that allows computation directly on encrypted data, regarded as the "Holy Grail". Compared to popular technologies like ZKP and TEE, it is relatively less talked about, mainly 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 raising over ten million dollars and undergoing several years of technical development, the market attention remains underestimated due to the limitations of FHE itself.
However, recently Mind Network has launched a number of favorable news around AI application scenarios. For example, its developed FHE Rust SDK has been integrated into 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 well in AI privacy computing? Can it achieve a leapfrog or redemption through the narrative of AI Agents?
In simple terms: FHE fully homomorphic encryption is a cryptographic technology that can directly act on the current public chain architecture, allowing arbitrary calculations such as addition and multiplication to be performed directly on encrypted data without the need to decrypt the data beforehand.
In other words, the application of FHE technology allows data to be fully encrypted from input to output, meaning that even the nodes responsible for consensus on the public chain cannot access plaintext information. This enables FHE to provide a technical foundation for training some AI LLMs in vertical segments such as healthcare and finance.
Making FHE a "preferred" solution for traditional AI large model training, rich expansion of vertical scenarios, and integration with blockchain distributed architecture. Whether it is cross-institution collaboration in medical data or privacy inference in financial transaction scenarios, FHE can serve as a complementary option due to its unique characteristics.
This is actually not abstract, and it can be understood with a simple example: for instance, an AI Agent as an application aimed at the consumer end usually integrates different AI large models provided by suppliers such as DeepSeek, Claude, and OpenAI in the backend. But how can we ensure that in some highly sensitive financial application scenarios, the execution process of the AI Agent will not be influenced by a large model backend that suddenly changes the rules? This inevitably requires the input Prompt to be encrypted, 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 based on fully homomorphic encryption (FHE) that Mind Network is trying to build. It includes enabling multiple parties to achieve efficient model training and inference through distributed computing power (GPU) without relying on a central server, providing consensus verification based on FHE for AI Agents, among other things. This design eliminates the limitations of traditional 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 the Mind Network's own distributed public chain architecture. For example, during special on-chain transaction processes, FHE can protect the privacy reasoning and execution processes of each party's Oracle data, allowing AI Agents to make autonomous trading decisions without exposing positions or strategies, etc.
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?
Previously, TEE was able to seize the opportunity of AI Agent due to the TEE hardware environment's capability to host data in a privacy-preserving state, which in turn allows the AI Agent to autonomously manage private keys, leading to a new narrative of asset management. However, there is a significant flaw in TEE's management of private keys: trust relies on third-party hardware providers (e.g., Intel). To enable TEE to function effectively, a distributed chain architecture is required to add an additional public and transparent "consensus" constraint to 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 yet widely used in the web3 ecosystem, it is already a very mature technology in the web2 field. In contrast, FHE will gradually find its value in both web2 and web3 under the current explosion of AI trends.
Above.
In summary, it can be seen that FHE, as a holy grail level encryption technology, is bound to become one of the cornerstones of security under the premise of AI becoming the future, with the possibility of being further widely adopted.
Of course, despite this, the cost issue of FHE in algorithm implementation must not be avoided. If it can be applied in web2 AI scenarios and then linked to web3 AI scenarios, it is believed that it will unexpectedly release a "scaling effect" to dilute overall costs, allowing for more widespread application.
The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
From "Holy Grail" to Cornerstone: How FHE is Reshaping the Web3 Privacy Computing Ecosystem?
I have previously mentioned in several articles that AI Agents will be the "redemption" of many old narratives in the Crypto industry. During the last wave of narrative evolution around AI autonomy, TEE was once placed at the forefront. However, there is another even more "niche" technical concept than TEE and even ZKP: FHE—fully homomorphic encryption, which will also gain "rebirth" due to the drive of the AI track. Below, I will clarify the logic through examples:
FHE is a cryptographic technology that allows computation directly on encrypted data, regarded as the "Holy Grail". Compared to popular technologies like ZKP and TEE, it is relatively less talked about, mainly 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 raising over ten million dollars and undergoing several years of technical development, the market attention remains underestimated due to the limitations of FHE itself.
However, recently Mind Network has launched a number of favorable news around AI application scenarios. For example, its developed FHE Rust SDK has been integrated into 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 well in AI privacy computing? Can it achieve a leapfrog or redemption through the narrative of AI Agents?
In simple terms: FHE fully homomorphic encryption is a cryptographic technology that can directly act on the current public chain architecture, allowing arbitrary calculations such as addition and multiplication to be performed directly on encrypted data without the need to decrypt the data beforehand.
In other words, the application of FHE technology allows data to be fully encrypted from input to output, meaning that even the nodes responsible for consensus on the public chain cannot access plaintext information. This enables FHE to provide a technical foundation for training some AI LLMs in vertical segments such as healthcare and finance.
Making FHE a "preferred" solution for traditional AI large model training, rich expansion of vertical scenarios, and integration with blockchain distributed architecture. Whether it is cross-institution collaboration in medical data or privacy inference in financial transaction scenarios, FHE can serve as a complementary option due to its unique characteristics.
This is actually not abstract, and it can be understood with a simple example: for instance, an AI Agent as an application aimed at the consumer end usually integrates different AI large models provided by suppliers such as DeepSeek, Claude, and OpenAI in the backend. But how can we ensure that in some highly sensitive financial application scenarios, the execution process of the AI Agent will not be influenced by a large model backend that suddenly changes the rules? This inevitably requires the input Prompt to be encrypted, 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 based on fully homomorphic encryption (FHE) that Mind Network is trying to build. It includes enabling multiple parties to achieve efficient model training and inference through distributed computing power (GPU) without relying on a central server, providing consensus verification based on FHE for AI Agents, among other things. This design eliminates the limitations of traditional 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 the Mind Network's own distributed public chain architecture. For example, during special on-chain transaction processes, FHE can protect the privacy reasoning and execution processes of each party's Oracle data, allowing AI Agents to make autonomous trading decisions without exposing positions or strategies, etc.
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?
Previously, TEE was able to seize the opportunity of AI Agent due to the TEE hardware environment's capability to host data in a privacy-preserving state, which in turn allows the AI Agent to autonomously manage private keys, leading to a new narrative of asset management. However, there is a significant flaw in TEE's management of private keys: trust relies on third-party hardware providers (e.g., Intel). To enable TEE to function effectively, a distributed chain architecture is required to add an additional public and transparent "consensus" constraint to 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 yet widely used in the web3 ecosystem, it is already a very mature technology in the web2 field. In contrast, FHE will gradually find its value in both web2 and web3 under the current explosion of AI trends.
Above.
In summary, it can be seen that FHE, as a holy grail level encryption technology, is bound to become one of the cornerstones of security under the premise of AI becoming the future, with the possibility of being further widely adopted.
Of course, despite this, the cost issue of FHE in algorithm implementation must not be avoided. If it can be applied in web2 AI scenarios and then linked to web3 AI scenarios, it is believed that it will unexpectedly release a "scaling effect" to dilute overall costs, allowing for more widespread application.