One of the greatest technological achievements in the cryptographic world is achieving transparency in trusted computing. Blockchain solves the most critical proposition in Satoshi Nakamoto's paper: how to build a permissionless and verifiable system in an untrustworthy environment through 'global consensus + complete openness'.
Thus, we gained a decentralized ledger and smart contracts—a publicly running, auditable, and tamper-proof program execution entity. This architecture is highly revolutionary but also lays down a paradox:
When intelligence needs to rely on data and data must be transparent, privacy is destined to be non-existent.
We began to build intelligence with transparency as a prerequisite. However, 'transparent intelligence' has inherent limitations. It can handle simple financial executions well, such as AMM, lending liquidation, and on-chain voting. But once intelligence wants to go further—such as behavior recognition, recommendation ranking, dynamic reputation assessment, and personalized interaction optimization—it must rely on more individual dimension data. Once this data is made public, individuals can no longer hide.
In the current architecture, if you want the contract to 'understand who you are', you must 'tell it who you are'; if you want the model to 'recommend for you', you have to 'expose your preferences'; if you want the service to 'dynamically adjust parameters', you must 'give up information sovereignty'.
Zero-Knowledge Proofs (ZKP) answer the question 'I know this secret, but I won't tell you.' They enable verification, but ZKP is not suitable for all scenarios. When faced with demands such as large-scale data analysis, AI model training, decentralized recommendations, and reputation systems, the 'proof-verification' framework of ZKP is far from sufficient; what we need is collaboration—data collaboration, not its leakage.
FHA (Fully Homomorphic Aggregation technology) was born for this purpose. It is an encryption computing paradigm designed for real on-chain needs. FHA primarily uses an additive encryption mechanism.
For example: if you have multiple ciphertexts Enc(x1), Enc(x2), ..., Enc(xn), you can directly perform addition on the ciphertexts to obtain Enc(x1 + x2 + ... + xn) without needing to decrypt.
This is the manifestation of 'homomorphism'.
It does not attempt to cover everything; rather, it precisely targets the 'aggregative intelligence' of on-chain data, allowing encrypted data to still 'speak', 'collaborate', and create value for users while protecting privacy. For example, if you interacted with ten protocols on-chain today, each DApp would want to understand your preferred behaviors, but you do not want to expose your address, assets, and history. What to do? Mind Network says, we do not decrypt your data; we use FHA technology to aggregate results from locally encrypted computations, only putting an irreversible, verifiable model output on-chain. This output cannot restore the original data but can be used for intelligent recommendations, dynamic weighting, and reputation assessment. What you provide is 'capability', not 'naked photos'.
This mechanism is a rebellion against the modern logic of 'data slavery'. In Web2, our data is extracted for free, fed to algorithms, and ultimately forms an inescapable black box society. However, FHA offers another path: the value of data can be utilized, but the power always belongs to the data owner.
Back to Mind Network, what can this product do?
Mind Network is the first re-staking solution designed for AI and PoS networks based on FHE.
Just as EigenLayer serves as a re-staking solution for the Ethereum ecosystem, Mind is a re-staking solution for the AI field. Through re-staking and FHE consensus security solutions, it ensures the security of the token economics and data of decentralized AI networks. Let me explain separately:
Consensus security: In AI and PoS networks with a smaller validator network scale, consensus security poses challenges and is vulnerable to collusion and fraud. MindNetwork uses FHE encryption to verify computations, ensuring fairness and security in the consensus process and preventing validators from influencing each other's results.
Data security: AI networks often involve sensitive and high-value data, requiring strict data security measures. Mind Network adopts end-to-end encryption, supports encrypted data calculations, and ensures data privacy and security during computation and verification processes.
Cryptoeconomic security: Tokens staked in AI and PoS networks are often subject to volatility, which affects network security. MindNetwork's Retaking solution supports various assets, including ETH, BTC, and blue-chip AI tokens, thereby reducing risk.
Mind Network has progressed beyond the conceptual level. They have already landed on the BNB Chain and reached deep cooperation with ZAMA, IO.NET, DeepSeek, etc., providing AI network consensus security services for io.net, Singularity, Nimble, Myshell, AIOZ, and offering solutions for FHE Bridge to Chainlink CCIP, as well as AI data security storage services for IPFS, Arweave, Greenfield, etc.
Returning to the initial question: Does AI in the cryptographic world have to come at the cost of transparency?
MindNetwork tells you: No need. Mind does not attempt to break the transparent logic of Web3; instead, it adds an abstract layer of 'encrypted intelligence' on top of it. In this layer, users no longer expose plaintext data but upload encrypted behavioral trajectories; the system no longer directly reads data but uses FHA technology to perform aggregation, training, and inference in encrypted state. The data never leaves the user's control, yet the entire system can operate with intelligence comparable to Web2, even yielding more trustworthy analytical results.
