Targeting the Core of AI: A Deep Dive into Binance-Invested FHE Project Mind Network
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Targeting the Core of AI: A Deep Dive into Binance-Invested FHE Project Mind Network
Mind Network is the first re-staking solution designed for AI and PoS networks based on FHE.
Navigating Web3 tides with focused insightsThe Holy Grail of Cryptography—Fully Homomorphic Encryption (FHE)
On May 5, Ethereum co-founder Vitalik Buterin once again shared on Twitter his 2020 article about FHE (Fully Homomorphic Encryption), reigniting interest and discussion around FHE technology. The article dives deep into the underlying mathematical principles; the original English version is available here.
FHE (Fully Homomorphic Encryption) enables computation on encrypted data without requiring decryption—making it one of the most cutting-edge areas in cryptography, often referred to as its "holy grail," alongside zero-knowledge proofs (ZK).
Simply put, fully homomorphic encryption allows direct computation on encrypted data—no decryption needed.
While 1+2 easily yields 3, the real breakthrough of FHE lies in being able to compute Encrypt(1)+Encrypt(2) and still obtain Encrypt(3)—ensuring that ciphertext computations equal the encryption of plaintext computations.
Unlike ZK, which primarily focuses on scalability in Web3, FHE emphasizes data privacy and security. While ZK rollups dominate current Web3 discourse, FHE is steadily revealing unique potential across multiple domains—especially AI.
Mind Network
Mind Network is the first restaking solution built on FHE, designed for AI and Proof-of-Stake (PoS) networks.
Just as EigenLayer serves as a restaking infrastructure for the Ethereum ecosystem, Mind plays a similar role in the AI space. By combining restaking with FHE-based consensus security, it ensures both token economic security and data security for decentralized AI networks.
The team behind Mind primarily consists of professors and PhDs specializing in AI, security, and cryptography from institutions such as Cambridge, Google, Microsoft, and IBM. A core team member was selected as one of only 12 Ethereum Foundation Fellows globally, collaborating closely with the Ethereum Foundation’s research team on cryptography and security. Their groundbreaking FHE + Stealth Address solution—MindSAP (research paper link, highly technical—read at your own risk)—solved the open problem posed by Vitalik in his Stealth Address Open Problem. This innovation attracted significant attention within the Ethereum community, leading to multiple academic publications and conference presentations.
In 2023, Mind Network was accepted into Binance Labs’ incubation program and completed a $2.5 million seed round led by Binance and other prominent investors. It also received an Ethereum Foundation Fellowship Grant, joined the Chainlink Build Program, and became an official Chainlink Channel Partner.
In February 2024, Mind Network became a key strategic partner of ZAMA, a leading cryptography company in the FHE space.
Recently, Mind Network has accelerated its ecosystem expansion, providing consensus security services for AI networks like io.net, Singularity, Nimble, Myshell, and AIOZ; delivering FHE-powered bridge solutions for Chainlink CCIP; and offering secure AI data storage services for IPFS, Arweave, and Greenfield.
FHE + AI: Tackling Core Challenges in Artificial Intelligence
At the Hong Kong Web3 Conference this April, Vitalik expressed optimism about FHE’s future applications in areas like encrypted voting. As a frontier of cryptography—and a direction aligned with Ethereum’s pursuit of cryptographic limits—FHE stands at the edge of what’s possible.
Recently, ZAMA’s founder published a "Master Plan" outlining the vision to build an end-to-end encrypted network called HTTPZ (“Z” for “Zero Trust”) and make FHE ubiquitous across blockchain and artificial intelligence.
Key stages in AI development—training, fine-tuning, usage, and evaluation—all face a common challenge in decentralization: how to eliminate trust assumptions. For example:
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During model training, cross-validation is required to select the best-performing model
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Before deploying AI services, rankings are needed to identify top-performing providers
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Models require continuous optimization and iteration, necessitating independent evaluation
In centralized settings, these processes rely on compliance and trust in large corporations acting honestly.
But in decentralized environments where no single entity provides credibility, verifying fair and effective collaboration among participants becomes a major hurdle—one where FHE shines.
For instance:
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During model training, anonymous voting via FHE can select optimal results—removing reliance on entities like OpenAI
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Before using AI services, blind scoring determines service quality—eliminating trust assumptions in centralized app stores
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For ongoing model tuning, random sampling checks enable trustworthy evaluations—removing dependence on third-party evaluators
FHE enables true zero-trust AI, overcoming ZK’s limitations that still require off-chain aggregation under certain trust assumptions.
There are many more examples where zero-trust enabled by FHE allows AI agents and multi-agent systems to achieve smarter interconnectivity and better governance.
Moreover, FHE’s unique ability to compute directly on ciphertext addresses two critical challenges: data privacy and data ownership:
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Who can access our data? = Data Privacy
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Who owns the data generated by AI? = Data Ownership
With FHE, user data remains encrypted on the client side and exists solely as ciphertext during storage, transmission, and computation.
Currently, outside of FHE, data can only be encrypted during storage and transit—but any computation requires decrypting to plaintext, thereby stripping users of control over their data. In practice, once your plaintext data is copied, others can duplicate it endlessly, and you have no way of knowing who uses it—relying solely on self-reporting or third-party audits. With FHE, even if ciphertext copies are made, decryption and access to the original data require explicit user consent. This empowers users to monitor data activity in real time, enabling data usability and monetization while preserving confidentiality—protecting both privacy and true ownership.
These capabilities are urgently needed in the convergence of AI and Web3—enabling public staking while achieving encrypted consensus, preventing malicious behavior and resource waste.
AI’s Next Big Thing
Clearly, the integration of AI and Web3 is inevitable. In this context, FHE’s role in AI could be as transformative as the “next big thing” was for Apple.
Recently, IO.NET and Mind Network announced a deep collaboration to jointly develop advanced solutions enhancing AI security and efficiency. IO.NET has integrated Mind Network’s FHE-based encryption into its distributed computing platform to strengthen product security.
For details on the partnership, see: Mind Network and io.net Partners up for Advanced AI Security and Efficiency
IO.NET sets a strong precedent for integrating AI with FHE.
Take IO.NET: users contribute computing power, and AI developers rent it.
When a developer submits a task, it gets split and processed across user-provided nodes.
This raises several key questions: Whose compute resources should be used? Are the results accurate? And does renting compute leak either party’s private information?
1. Whose Compute Resources Should Be Used?
Typically, node selection involves test jobs—randomly assigned tasks to check availability and readiness.
However, this opens opportunities for manipulation—nodes may collude or prioritize certain requests, akin to MEV attacks.
Mind addresses this with an FHE-based fair distribution mechanism: since all requests and data are encrypted, nodes cannot bias their responses based on content.
2. Is the Computed Result Correct?
In distributed computing, ensuring correctness typically relies on consensus through voting.
If nodes know each other’s votes in advance, copycatting ("herding") may occur, compromising result integrity.
With FHE, vote outputs remain encrypted between nodes, yet still contribute correctly to final computation—preserving fairness and accuracy.
3. Does Renting Compute Leak Privacy?
At its core, FHE ensures data security—keeping both the computation and input data encrypted throughout, thus eliminating privacy leaks entirely.
Viewing Through the Lens of Restaking
IO.NET itself functions as a PoS network: nodes must stake IO tokens to earn rewards from contributed compute.
A potential issue arises when staked token prices fluctuate significantly—jeopardizing validator incentives and network security.
Mind’s solution? Dual Staking—or even triple staking.
It supports staking with liquid-staked BTC/ETH and blue-chip AI network tokens, diversifying risk and boosting overall network security—an evolved form of shared security through restaking.
Additionally, Mind supports Remote Staking, allowing LST/LRT assets to participate without actual cross-chain transfers—maintaining asset safety.
Recently, Mind concluded its Glaxe testnet campaign, drawing over 650,000 active users and generating 3.2 million testnet transactions.
According to official updates, Mind’s mainnet protocol will launch soon—stay tuned.
Conclusion
Overall, while Mind talks about FHE and AI, the central theme is clearly “security”—using cryptography to solve fundamental security challenges.
Restaking ensures token economic security; Remote Staking protects asset security; FHE safeguards data security; AI + FHE enables consensus security.
The edifice of blockchain is built upon cryptography—and perhaps its future answers lie there too.
Beyond AI networks, Mind Network is expanding its solution scope—partnering in decentralized storage, EigenLayer AVS networks, Bittensor subnets, cross-chain bridges, and more—demonstrating FHE’s vast potential.
In Web3 2024, if ZK kicked off the year, FHE is poised to become the dominant narrative in the second half. Meanwhile, AI remains red-hot. With the combined narratives of AI, FHE, and restaking—and backed by support from the Ethereum Foundation and Binance—can Mind emerge as the leader in FHE? As the mainnet launch approaches, we’re about to find out.
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