One, what is FHE? And what is Mind Network?
In the blockchain world, common projects are either Layers (like Ethereum) or Protocols (like Uniswap). But what exactly does 'Network' mean in Mind Network? What category does it belong to?
In fact, Mind Network is more like a new 'infrastructure'. If we say:
- Ethereum provides consensus.
- Chainlink provides data pricing.
- Therefore, the Mind Network is: providing privacy protection and security assurance for AI.
In the future, AI will be ubiquitous, and Mind Network is the system support that provides these AIs with underlying data encryption capabilities and security trust mechanisms. It is not a single-function DeFi protocol but a set of trust infrastructure that can be embedded into any system.
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Two, what exactly is FHE?
FHE, short for Fully Homomorphic Encryption, is an encryption technology that allows computation while in an encrypted state.
In simple terms:
Traditional encryption = locking a safe, and you can only operate on the contents after unlocking it.
FHE = processing letters without having to open the safe.
This means: data remains encrypted throughout the entire process of transmission and processing, allowing the service provider to offer functionality without being able to peek into the content.
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Three, the fundamental differences between ZK and FHE.
People often ask: What is the difference between FHE and Zero Knowledge (ZK)?
- ZK validates 'the results after encryption';
- FHE performs operations on 'data during the encryption process'.
ZK is like 'I tell you I computed correctly, but I won't show you the process'; whereas FHE is 'I completed the task directly on something you can't understand'.
FHE further addresses the 'private shared state' problem that ZK cannot handle—such as in an anonymous voting system, where each user's voting content should remain confidential, but the system needs to calculate the accurate total result. FHE allows users to directly submit their ballots encrypted during voting, enabling the system to complete the entire counting process without decrypting each vote. Ultimately, only the voting results are decrypted and made public, while each voter's choice remains confidential throughout the process, truly achieving a combination of privacy protection and trustworthy results.
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Four, AI in a trust crisis: How can FHE break the deadlock?
With the widespread application of AI Agents, AI systems are developing towards multi-agent systems (MAS). In this trend, AI is no longer a service relationship between a single model and a person, but a complex ecology composed of multiple Agents. Agents need to understand human instructions and also collaborate with other Agents to complete tasks.
Therefore, trust and data security become core issues:
1. How can we ensure that communication data between humans and Agents is not leaked?
2. How can Agents collaborate without exposing each other's internal information?
In our vision, each Agent can be a node on the chain. When Agent A wants to collaborate with Agent B, it can send private data after encrypting it. Only the transaction record can be seen on-chain, and the specific content cannot be viewed, thus achieving a combination of privacy protection, transparency, and immutability.
FHE plays a key role in this process:
- Protect the privacy of communication between users and Agents.
- Ensure that all input and output are encrypted throughout the collaboration of multiple Agents.
This not only enhances the security of the system but also represents the first step towards a trustworthy Agent world.
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Five, practical case: How to use FHE to verify the reliability of AI models?
Taking the cooperation between Mind Network and DeepSeek as an example:
When a user calls an AI model, it is impossible to determine whether the model has been tampered with. An open-source model could be 'fine-tuned' into a biased version, and the user may be completely unaware.
Here, FHE + multi-node scoring mechanism is introduced:
- Each node scores the model's output, but the scoring results are encrypted.
- Nodes cannot plagiarize from each other, ensuring independent judgment.
- Finally, confirm whether the model is trustworthy through encrypted consensus.
This process not only prevents cheating but also enhances the credibility of model validation.
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Six, multi-model collaboration: How does FHE protect complex agent interactions?
When users simultaneously call multiple models (e.g., DeepSeek + Gemini):
- User inquiries are encrypted.
- Each model independently generates encrypted answers.
- The results of multiple models are returned after reaching consensus through FHE.
This brings two benefits:
1. User data privacy is protected throughout the entire process;
2. The multi-model consensus mechanism increases the credibility of the answers.
In more complex AI applications, FHE can ensure the independence and trustworthy collaboration of every component in the Agent network, addressing the risks of data leakage and manipulation.
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Seven, conclusion: FHE + blockchain, building a trustworthy AI infrastructure.
Blockchain makes data immutable and verifiable but lacks privacy protection. FHE enables on-chain data processing to be 'invisible but computable', achieving a combination of privacy and trust.
The Mind Network was born for this new world:
- It is not an application, nor a single-point protocol;
- It provides security assurance for the entire AI ecosystem, Agent network, and all future intelligent systems as an 'invisible foundation'.
Just as Ethereum is to consensus and Chainlink is to data, Mind Network is the gatekeeper of privacy and trust.
