Written in advance:

From recent new coin launches, we can see that Binance Alpha is striving to change the traditional pattern of falling after launch. The moment of true change is when $FHE launched at a low market value, welcoming the airdrop dump, and then started to slowly rise. The current market value is relatively low, at just over twenty million, which is a comparatively comfortable starting point for a technology project.

The current market's hotspots are mainly focused on MCP, which involves multi-AI agent collaboration, while FHE technology has significant implications for protecting privacy in multi-AI agent collaboration.

This article is a colloquial introduction to $FHE and its project Mind Network for novice cryptocurrency traders, participating in #MindNetwork全同态加密FHE重塑AI未来 essay activity, DYOR.

I. What is FHE? Simply put

FHE, fully known as 'Fully Homomorphic Encryption', can be explained in one sentence: data is encrypted throughout the process, and computing and collaboration can still be completed. This means that it allows AI to analyze, predict, and make decisions without exposing data, achieving 'working while encrypting'.

This technology itself is quite hardcore, but for users, you only need to know: it makes AI trustworthy, privacy-preserving, and transparent in its black box.

II. What is the Mind Network project doing?

Mind Network is the first FHE blockchain project designed specifically for AI agents, incubated by Binance Labs, funded by the Ethereum Foundation, with investments from Animoca, Chainlink, and others, possessing both technical and financial strength.

It has proposed and implemented a new concept: AgenticWorld—this is a world composed of millions of AI agents that can think independently, work collaboratively, and protect privacy.

Mind Network provides the foundation for this 'AI world': FHE secure computation, consensus mechanism, encrypted communication, on-chain collaboration platform (MindChain), and a unified agent interaction protocol Hub system.

ZK (Zero-Knowledge Proof), MPC (Multi-Party Computation), and FHE (Fully Homomorphic Encryption) technologies have some similarities in certain applications. Here’s a brief summary of the characteristics between various technologies:

ZK can prove correctness without disclosing information, protecting privacy, and is commonly used for identity and permission verification;

MPC supports multiple parties to compute collaboratively while keeping data confidential, making it useful in cross-institutional data analysis and financial auditing.

FHE has significant advantages in AI, allowing data to remain encrypted throughout the computation process. This means that even if encrypted data is handed to a third party for auxiliary computation during AI data processing and model training, there is no need to worry about data leakage, greatly enhancing data security and privacy for AI, and promoting the application of AI technology in fields with high data security requirements.

Take a look at an official comparison chart

If you don’t understand it, let me give you a simple example:

Today, if you suddenly want to look at adult content, but the website requires you to be over 18 and to fill in your exact birthday, ZK can directly tell the website through encrypted identification that you are an adult without needing to fill in your specific birthday. However, ZK needs to specifically design a password for the fact that you are over 18, making it more suitable for important but simple data like identity information.

MPC and FHE can lay out more computational data on this basis. For example, if you're worried about your health due to late-night cryptocurrency trading and you want to go to the hospital for a check-up, after a series of tests, you may not want the attractive female doctor to know that your kidneys are not functioning well. In this case, MPC and FHE can encrypt the entire computation process, ensuring that all collaborating departments receive only encrypted data, and after computation, you receive the results in complete confidentiality.

In this process, the approach of MPC is that each medical department only knows its own data, requiring multiple departments to encrypt and derive results. The FHE approach is that the encrypted results from multiple departments are handed over to a comprehensive data computation entity to derive the final conclusion.

Now you can go ask the female doctor for her contact information (laughs)

IV. What pain points does it actually solve?

We can deduce from 'what users are most concerned about':

"I don't want AI to know all my privacy"

Mind Network uses FHE to ensure that AI can only see encrypted data but can still compute.

You can show AI the encrypted version of transaction records and medical reports; it can still provide you with investment advice or health analysis—without seeing the original data.

"How do AI and AI collaborate without leaking secrets?"

Different AI agents connect tasks through the Hub, with all information encrypted using FHE, requiring no trust throughout the entire process, also preventing any eavesdropping.

"How does AI know that others are not deceiving me?"

Mind Network supports 'verifiable computation': You can verify that the AI made decisions using genuine models, rather than just throwing something together.

"How do cross-chain and cross-AI systems collaborate?"

Mind Network connects multiple chain systems, allowing AI on chains like Ethereum, Solana, BNB Chain, etc., to collaborate seamlessly while maintaining privacy through FHE.

Now, looking back at the hospital, can you walk in confidently?

V. What are the key application scenarios?

To understand this issue, what needs to be answered is

"Will you let AI access your identity data?"

In other words, would you trust a 'black box AI' to handle your sensitive information?

If the answer is negative, then the solution provided by FHE is:

You authorize AI to use encrypted data; it cannot decrypt it but can only analyze within the scope you specify;

You hold the key throughout the entire process, and can control to cancel, disconnect, or stop usage at any time;

The granularity of authorization is very fine; it can be 'one-time' or 'only when I am online'.

In practical applications, we can see that:

🔐 AI + Privacy

Healthcare: AI reads encrypted health check reports and provides health advice;

Finance: Encrypted asset data, generating portfolio optimization;

Chat/recommendation systems: Read encrypted social preferences without exposing privacy.

Case: The 'World AI Health Hub' in collaboration between Mind Network and ZAMA allows hospitals to share patient data in encrypted form, with AI models analyzing diagnostic suggestions without exposing raw information.

Value: Protecting patient privacy and promoting cross-institutional medical AI collaboration.

🤖 AI + Decentralization + Multi-Agent

In the DeFi field: Multiple AI agents conduct risk analysis, arbitrage, and loan portfolio optimization based on on-chain data;

Games: AI NPCs can interact with players and remember player behavior while safeguarding data from leakage;

DAO governance: Anonymous voting, consensus modeling, and on-chain governance suggestions are encrypted and verifiable throughout the process.

Case: Collaboration with Phala Network, combining TEE (Trusted Execution Environment) and FHE to ensure the privacy of game AI decisions.

Value: Protecting player behavior data and virtual assets, achieving secure transactions and identity verification in a decentralized gaming economy.

Enterprise-level AI collaboration:

Solution: The Swarms Shield system supports encrypted communication among multiple Agents to prevent business secrets from leaking, applicable to supply chain management and financial analysis.

VI. How does FHE build infrastructure for AI Agents?

In the architecture of Mind Network, each agent can possess the following capabilities:

Identity recognition: Ensures that you and your AI are independently identifiable individuals through the on-chain identity system;

Secure environment: FHE ensures that AI will not expose your data;

Decentralized deployment: AI can run on any node, avoiding monopolization by large platforms;

Verifiable computation: Any output can be verified for its authenticity and accuracy through ZK/FHE;

Controllable authorization: You can specify which data can be accessed by which AI and when, preventing unauthorized use.

So, what conditions does the future AgenticWorld need?

The three core keywords are:

Data security: Must ensure that "AI can see but cannot steal";

Computational transparency: How AI arrives at conclusions must be verifiable;

Trustless cooperation: AI can 'encrypt handshake' with each other, collaborating without needing to trust one another.

FHE plays a decisive role in these three points, forming the true encrypted foundation that supports the coexistence and cooperation of millions of agents.

VII. How do we view the combination of AI and blockchain? What is the future vision?

AI is the brain, blockchain is the skeleton, and FHE is the skin—without FHE, data runs naked, and no matter how smart AI is, it is difficult to implement.

Consensus mechanism among agents: How do AIs unify decisions and reach voting results? FHE can ensure that the voting process is completely encrypted while the results are authentic and trustworthy.

Multi-chain collaboration: FHE Bridge allows different chain AI agents to share data while ensuring privacy is not compromised.

End-to-end encryption: Without FHE, the so-called 'end-to-end' is actually settled by a centralized server; with FHE, AI truly computes on the user end, and privacy does not leave the device.

Currently, Mind Network is advancing two visionary protocols:

DeCC (Decentralized Confidential Computing): Ensures that any computation does not rely on central servers;

HTTPZ: The next generation 'zero-trust' Internet protocol, encrypting the entire data lifecycle.

Behind these visions, FHE is the only feasible underlying technology support—without FHE, everything is just pseudo-encryption.

VIII. Is it worth investing? My advice

From a narrative perspective: AI + Security is the major direction for the next 10 years. Mind Network happens to hit the three major narrative intersections of AI Agents, privacy computing, and FHE.

From the capability perspective: It has already established partnerships with DeepSeek, Chainlink, io.net, Phala, Swarms, etc., and both the mainnet and products are online with clear applications.

From the token mechanism perspective: $FHE has completed TGE, and staking to activate Agents has formed a complete positive incentive model with high APY and practical participation.

From a risk perspective: FHE technology still faces engineering challenges, although it has been implemented, it still encounters performance bottlenecks; attention should be paid to the gap between short-term speculation and long-term development.

✅ Investment advice:

Medium to long-term layout configuration:

If you are among those who are optimistic about the future development of the AI ecosystem, you can participate in Agent training and collaboration through staking $FHE, earning while playing, and engaging deeply.

Currently, FHE is holding a staking activity, reportedly with an annualized return of up to 400%. You can participate by logging into the Mind Network official website, staking more than 10 $FHE and locking it for over a month to launch your own on-chain AI agent. This person has not tried it, but teachers looking to understand can find relevant bloggers' articles.

Short-term investor advice:

Purely technical consideration: If the flag pattern is established, a quality risk-reward position is at 0.089. If the flag pattern descends, look for horizontal support at 0.078 to observe price reactions. The short-term target looks to be around 0.12.

Thank you for reading; please correct any errors, DYOR.