With the widespread application of decentralized AI in finance, healthcare, identity verification, and other fields, the issue of user data privacy protection has increasingly become a focal point. Compared to traditional centralized systems, data in the Web3 world is more open, with stronger liquidity, but is also more easily exposed to untrustworthy environments.

In this context, end-to-end encryption (E2EE) and fully homomorphic encryption (FHE) are seen as two key technologies, respectively playing roles in 'data transmission security' and 'data computation security.' Only by combining the two can a truly secure and trustworthy AI computation system be built.

1. E2EE and FHE: solving data privacy issues at different stages.

End-to-end encryption (E2EE) solves privacy risks 'during transmission.'

E2EE is a familiar encryption method, commonly used in communication software like WeChat, Signal, and WhatsApp. Its basic principle is that once data is encrypted by the sender, even if it passes through multiple parties during transmission, only the receiver can decrypt and use it.

In AI applications, E2EE can be used for:

Data transmission between users and decentralized AI networks;

Input of anonymous identity verification information;

On-chain transmission of sensitive behavioral preferences, asset structures, and other content.

The advantage of E2EE is that it prevents 'interception during transit,' but once data reaches the processing party (like an AI agent), it must be decrypted for use, exposing risks again.

Fully Homomorphic Encryption (FHE) solves privacy risks 'in use.'

The strength of FHE lies in its ability to perform computations directly on encrypted data without decryption, with the final output remaining in an encrypted state, only authorized parties can decrypt.

For example: you can hand over a locked piece of data to an AI for processing; it can complete the computation but cannot see the content. Ultimately, you receive back the 'processed lock,' using your own key to open it and see the result.

In decentralized AI scenarios, this means:

User data does not need to be exposed to on-chain contracts, nodes, or AI agents;

AI can complete reasoning, judgment, and modeling in an encrypted state;

Even with multi-chain cooperation and cross-chain transmission, data remains in a 'black box' state.

2. Why do the two need to be combined?

E2EE and FHE each have advantages, but also have their own 'blind spots.'

Technology Advantages Blind Spots

E2EE prevents data from being intercepted or tampered with during transmission; data must be decrypted upon reaching the endpoint, exposing the computation process.

FHE keeps the data computation process always encrypted, preventing internal leaks; it does not handle the transmission security when 'sent from the user side.'

In decentralized AI systems, data must not only be securely 'transmitted' but also securely 'utilized.' At this point, the combination of 'E2EE + FHE' becomes a full-chain privacy protection solution:

Users locally encrypt data → securely upload via E2EE;

AI performs computational processing on-chain via FHE → encrypted results returned;

Users or authorized nodes decrypt to obtain results, completing business processes.

Throughout the process, there is no need to expose original data or computational details at any stage.

3. Examples combining application scenarios

1. Medical consultations: zero leakage of sensitive data

Users input personal symptoms and medical records, AI locally uploads them via E2EE encryption; FHE ensures medical AI completes preliminary analysis and judgment in an encrypted state, returning suggestions, and the entire process prevents hospitals and network nodes from viewing original information.

2. On-chain investment advisory services: asset privacy + strategy confidentiality

A user entrusts a decentralized AI to optimize asset allocation, encrypting and transmitting their asset information and preferences via E2EE; FHE allows the AI to reconfigure strategies on-chain based on encrypted data, keeping the entire operational logic and asset situation completely confidential to prevent opponents from following trades, arbitraging, or attacking.

3. Decentralized Identity Verification (DID)

Users provide a set of identity credentials for the on-chain AI system to judge whether they meet a certain qualification (e.g., participating in a certain DAO governance); data is uploaded using E2EE, logical judgments are executed using FHE, and the process of 'verification passed' or 'verification failed' is completely private, without exposing identity, history, or wallet address.

4. The technology behind it: the real demand for a zero-trust environment

The Web3 world is a typical 'zero-trust environment':

Nodes may be anonymous;

Smart contracts cannot be altered once deployed;

Data frequently circulates during multi-chain interactions.

This determines that we can no longer rely on centralized servers to 'protect data' and must build a trust at the technological level. FHE and E2EE are the core components of this level.

5. Future vision: FHE + E2EE driving a 'Privacy Intelligent Network'

As the computational capabilities of FHE continue to improve and the E2EE toolchain gradually standardizes, we will see in the future:

A completely privacy-based data market;

AI services supporting encrypted input, encrypted processing, and encrypted output;

Users no longer worry about data being exposed when calling AI agents on any chain or any device.

This is the prototype of the 'Privacy Intelligent Network'—obtaining the most trustworthy intelligent services without revealing any information.

Summary

FHE and end-to-end encryption are not alternatives; they are complementary. The former ensures data remains encrypted while being 'used,' and the latter ensures data is not monitored while being 'transmitted.'

In an AI-driven decentralized world, only by combining the two can we truly achieve full-chain privacy protection from users to the chain, from data to decision-making, paving the way for a secure future for AgenticWorld.

If you truly want to embrace a future of privacy, security, and trustworthy AI, then this technological combination is an insurmountable moat.