The Combination of FHE and End-to-End Encryption: The Necessity of Strengthening AI Privacy Protection
In the world of decentralized AI, data security is not just a matter of the "processing stage" but a challenge that spans the entire "chain of transmission." User data, from sending, transmitting, storing, to the AI agent executing computations, can become a target for attackers at every stage.
To protect user privacy, we usually use end-to-end encryption (E2EE) to ensure that data is not eavesdropped on during transmission. However, relying solely on E2EE is not enough, especially when data arrives at the AI or on-chain system, which still needs to be decrypted to be used. It’s like: your package is sealed, and no one can peek during transport, but once it arrives at the recipient, it is opened directly, exposed to the light.
At this point, FHE (Fully Homomorphic Encryption) becomes the critical "second lock."
1. E2EE + FHE: Dual Encryption Protection Mechanism
E2EE is responsible for protecting data during its transmission from the user to the AI system, preventing it from being eavesdropped or tampered with in the middle.
FHE is responsible for protecting data during its internal use within the AI system, allowing computations to be completed without decryption, even in on-chain or AI nodes.
This achieves full-process confidentiality from "end" to "end": not only is the transmission encrypted, but the computation is also encrypted.
2. Why is this combination particularly important for decentralized AI?
In centralized AI systems of Web2, user data is often concentrated in the cloud, and the processing is centrally controlled. However, in the AI network of Web3, data may flow through multiple nodes, multiple chains, and multiple agents. If any part of the data is decrypted, there exists a huge risk.
E2EE can prevent data from being eavesdropped on during transmission, while FHE ensures that data remains encrypted during computation. The combination of the two acts like putting two layers of bulletproof vests on the data.
3. Examples of Application Scenarios
AI Medical Consultation: Users input sensitive health information; E2EE protects transmission security, and FHE allows the AI to analyze conditions while in an encrypted state, protecting user privacy.
AI Investment Advisory Service: Users upload asset statuses and risk preferences, with encrypted transmission and encrypted computation throughout, preventing sensitive asset information from being leaked.
