By: Wublock
The strength of AI Agents comes from autonomy, which must be built on security. Mind Network, centered on FHE, provides a new path for agents to collaborate without exposing data, solving the trust dilemma.
The latest IDO project Mind Network on Binance Wallet has received investment from Binance Labs.
AI Agents have become one of the most important hotspots over the past year, giving rise to star projects such as Virutual and ai16z. NVidia CEO Jensen Huang has also publicly supported AI Agents, stating that 'AI Agents may be the next robotics industry, with potential reaching trillions of dollars.' Recently, OpenAI released a brand-new toolkit designed to simplify AI Agent application development, providing substantial development support for complex AI Agents. In 2025, the potential for AI Agents may continue to explode, with various intelligent agents capable of autonomous decision-making and collaborative work accelerating towards practicality.
However, as the capabilities of AI Agents surge, their challenges to user privacy and data security become increasingly prominent. To make them truly trustworthy, the technical community has begun to turn its attention to cryptographic solutions such as ZK, MPC, and FHE — we still remember the valuation explosions brought by various projects stemming from ZK. In contrast, FHE (Fully Homomorphic Encryption) has not yet gained sufficient attention in the market — its potential has yet to be fully tapped. In scenarios like AI Agents that need to handle vast amounts of sensitive data, FHE is expected to shine, opening up new spaces for privacy computing applications.
The Mind Network project, born out of the above context, is attracting industry attention. Mind Network is the first project to apply FHE technology to AI infrastructure (incubated and invested by Binance), with core highlights including the introduction of end-to-end encrypted computing architecture in Multi-Agent systems. This article will analyze the technical architecture, operational mechanism, and practical cases of Mind Network, exploring the application value of FHE in the AI Agent industry.
I. Introduction to Mind Network
Basic Information on Mind Network
Mind Network was established in 2022, with core team members coming from fields such as cryptography, blockchain, and artificial intelligence. At that time, with the rise of Web3 and artificial intelligence, the issues of data security and privacy became prominent. Mind Network builds a secure and efficient network platform based on FHE (Fully Homomorphic Encryption) technology, offering unique solutions for data sovereignty protection, fair consensus, private voting, secure cross-chain transmission, and trusted AI, aiming to lead Web3 into a new era of quantum resistance and end-to-end encryption.
Mind Network is dedicated to building a 'Trust Operating System' to support the safe operation of AI Agents with autonomous decision-making capabilities. Its goal is not only to provide 'safer AI' but also to construct a trusted AI infrastructure that can coexist with human society. Through the FHE network, it aims to achieve 'data can be computed in an encrypted state,' fundamentally addressing the four major security challenges faced by Agents:
1. Consensus security: By utilizing an encrypted consensus mechanism, it ensures that the behavior of agents during collaboration is verifiable and immutable;
2. Data Security: Agents must always keep data encrypted when processing sensitive data such as health and finance to avoid privacy breaches.
3. Computational security: Avoiding 'black box model' risks, achieving transparency and auditability in the computation process;
4. Communication Security: Achieving end-to-end secure communication through zero-trust encryption protocols.
Mind Network completed a $2.5 million seed round of financing in 2023 and a $10 million Pre-A round in 2024, raising a total of $12.5 million, with participation from renowned institutions like Animoca Brands. It was selected for Binance Labs' fifth-season incubation program and the Chainlink BUILD program, and has engaged in technical cooperation with Zama, Chainlink, etc., launching technical standards and products such as HTTPZ and MindV Hubs, focusing on building a secure, encrypted, and sustainable ecosystem. Its mainnet was officially launched in November 2024, with the TGE set for 2025.
Principles of FHE Technology
The FHE (Fully Homomorphic Encryption) technology adopted by Mind Network enables various calculations to be performed directly on encrypted data, with the computation results remaining encrypted without decryption throughout the process, greatly protecting privacy and security. Traditional encrypted computations require decryption beforehand, posing security risks. In multi-party data cooperation scenarios, traditional encryption struggles to ensure privacy, whereas FHE allows encrypted computations with data from various parties, with only authorized parties able to obtain plaintext using keys.
The specific working mechanism of FHE technology includes three processes: encryption, computation, and decryption. During the encryption phase, the sender uses a specific encryption algorithm and public key to convert plaintext into ciphertext. In the computation phase, computational nodes can perform operations such as addition and multiplication on ciphertext, relying on homomorphic properties to ensure that the results of ciphertext calculations are consistent with the encrypted results of the plaintext calculations. In the decryption phase, only the recipient with the private key can restore the ciphertext to plaintext.
The technological advantages of FHE have enormous application potential across multiple fields, effectively preventing data leakage in scenarios such as medical data sharing and joint risk assessment by financial institutions. It is currently widely applied in finance, cloud computing, artificial intelligence, and the Internet of Things.
ZK (Zero-Knowledge Proof), MPC (Multi-Party Computation), and FHE (Fully Homomorphic Encryption) technologies share some similarities in certain applications. Here is a brief summary of the characteristics among these various technologies:
ZK does not need to disclose information to prove its correctness and can protect privacy, commonly used in identity and permission verification; MPC supports multiple parties to compute collaboratively while keeping data confidential, which is very useful in cross-institutional data analysis and financial audits. FHE, however, has notable advantages in AI, allowing data to remain encrypted throughout the computation process. This means that during AI data processing and model training, even if encrypted data is handed to a third party for assisted computation, there is no worry of data leakage, greatly enhancing the security and privacy of AI data, aiding the promotion of AI technology in areas with high data security requirements.
Mind Network utilizes FHE (Fully Homomorphic Encryption) technology to enable Agents to complete collaborative tasks without exposing raw data. This can be summarized into four core security requirements:
· Consensus Security: In a decentralized environment, it is essential to ensure consistency among Agents. Current blockchains are fundamentally based on 'transaction' bookkeeping, which is challenging to satisfy complex dynamic collaborations. Mind Network provides a trusted collaboration mechanism based on FHE.
· Data security: Protecting Agents from exposing original content while processing sensitive data
· Computational Security: Providing encryption during the inference process while keeping it verifiable for Agents.
· Communication Security: Ensuring that Agents encrypt their communications or collaborations from transmission to results.
HTTPZ: The next-generation Internet protocol
HTTPZ is the next-generation Internet protocol, jointly proposed by Mind Network and ZAMA, aiming to utilize Fully Homomorphic Encryption (FHE) technology to achieve end-to-end encryption of network data.
In 2024, Telegram modified its terms of service after the arrest of founder and CEO Pavel Durov, submitting more user information to the U.S. government. This incident highlights that even platforms like Telegram, renowned for encryption technology and high confidentiality, find it difficult to completely protect user information under external pressure. HTTPZ, employing FHE, provides end-to-end encryption to ensure data privacy during transmission, storage, and computation. In other scenarios, such as in medical data contexts, hospitals upload encrypted genomic data for analysis using HTTPZ, with data being encrypted throughout, allowing cloud providers not to access the original data, effectively protecting data privacy.
Compared to traditional HTTP and HTTPS, the advantages of HTTPZ can be summarized as follows:
· Encryption Scope: HTTP lacks encryption mechanisms, with data transmitted in plaintext, making it susceptible to theft and tampering. HTTPS only encrypts during transmission, requiring decryption during data processing and storage, leading to data exposure risks. HTTPZ, on the other hand, utilizes FHE technology to achieve encryption throughout the entire lifecycle of data during transmission, storage, and computation.
· Architectural model: HTTP and HTTPS are based on traditional trust models, relying on the credibility of servers and intermediaries. HTTPZ adopts a zero-trust architecture, presuming no trust and conducting strict validation and authorization for each request and data interaction.
· Application support: HTTP and HTTPS struggle to meet the security requirements of decentralized applications (dApps), AI-driven solutions, or quantum-resilient systems. HTTPZ can enable secure, decentralized applications and quantum-safe encryption, effectively supporting emerging technologies such as blockchain, AI, and quantum computing.
With the rapid development of network environments and AI technologies, the zero-trust architecture and advanced encryption technology of HTTPZ can serve as critical support protocols for emerging technologies such as Web3, AI, blockchain, and quantum computing, providing a secure operating environment, promoting the Internet towards decentralization and intelligence, and leading the next generation of the Internet towards a safer, privacy-focused, and efficient direction.
II. Analysis of the Multi-Agent Consensus Issues in AI Agents
Comparison of Single Agent and Multi-Agent
With the widespread application of AI Agents and the handling of increasingly complex issues, the existing Single Agent often struggles to complete tasks efficiently and accurately, leading to the emergence of Multi-Agent. Multi-Agent, or 'multiple agents' or 'multiple intelligences,' can break down complex tasks through the collaboration of multiple Agents, leveraging each Agent's expertise to solve problems from different angles, achieving information sharing and collaborative work. This not only greatly enhances the ability to handle complex issues but also strengthens the system's flexibility and adaptability, providing powerful technical means to solve complex real-world problems.
Shortcomings of Single Agent:
· Limited capability scope, struggling to address complex tasks
· Lack of cross-validation can easily lead to judgment bias
· Independent operation, unable to rely on external forces
· Performance is prone to decline when the task volume is too large
Advantages of Multi-Agent:
· Professional division of labor, leveraging each other's strengths
· Information sharing, forming a complete solution
· Mutual verification, reducing error rates
· Flexible expansion, adapting to complex needs
Based on the advantages of handling complex tasks, Multi-Agent has been widely applied in scenarios including: Questflow, MetaGPT, ai16z, Swarms, etc.
Multi-Agent Consensus Issues
In the system structure of Multi-Agent, the consensus issues between different Agents are crucial. For example, in autonomous driving, consensus among perception Agents, decision-making Agents, and control Agents is particularly important. In the event of an emergency, such as a pedestrian suddenly appearing ahead, if the Agents cannot quickly reach consensus on emergency braking, it could likely lead to traffic accidents, threatening lives.
Data security and privacy issues cannot be ignored. In the healthcare industry, AI Agent systems come into contact with a large amount of sensitive patient information, such as medical records and diagnostic results. Once data security protections are insufficient, this private data could be leaked, which not only harms patient rights but also hinders the promotion and application of AI Agents in the healthcare field.
Decision consistency and efficiency are also important issues. In intelligent investment decision-making systems, different AI Agents may provide contradictory investment advice based on different algorithms and data, leaving investors confused.
III. Mind Network's Solutions Based on FHE Technology
Mind Network is committed to leveraging FHE technology to create a fully encrypted Web3, providing innovative solutions for the challenges faced by Multi-Agents or AI Agents, primarily covering the following key aspects:
1. Data sovereignty protection: The data processed by AI Agents often contains high-value information such as personal data, sensor data, transaction data, etc., with high sensitivity in input and output data. By implementing end-to-end encryption with FHE technology, calculations and processing can be completed without decryption, ensuring the security of data during transmission, storage, and use, ensuring that data sovereignty belongs to the user, and avoiding the risk of data leakage.
2. Fair Consensus Mechanism: For the consensus mechanism between AI networks and AI Agents, traditional voting methods are prone to cheating and manipulation in networks with fewer nodes. Leveraging FHE technology enables Agents to conduct consensus verification based on encrypted data, improving consensus efficiency and reliability, reducing cheating behaviors, and ensuring that the network reaches a consensus fairly and justly.
3. Communication Interaction Security: In multi-party or cross-chain collaborations, it is challenging for different intelligent agents to trust each other. For instance, having Binance's Agent collaborate with OKX's Agent, both sides are often reluctant to share data with one another. FHE technology allows them to exchange and process information without exposing data, protecting privacy while ensuring security and establishing a foundation of trust for cooperation.
4. Trusted AI Support: Mind Network empowers AI Agents through FHE technology, ensuring that data remains encrypted during data processing and model training, preventing data leakage, allowing AI Agents to efficiently process sensitive data in a secure environment, enhancing the security and privacy of AI data, and promoting the development of trusted AI.
Application Cases
· io.net: In April 2024, io.net announced cooperation with Mind Network to co-create solutions focused on enhancing AI safety and efficiency. io.net will introduce Mind Network's FHE solution into its distributed computing platform to enhance product security and further improve its ability to address the global GPU shortage.
· Chainlink: In May 2024, Chainlink and Mind Network established a strategic alliance to build FHE interfaces on Chainlink's Cross-Chain Interoperability Protocol (CCIP), applicable to various platforms like Arbitrum, Ethereum Foundation, and Polygon.
· Phala: In January 2025, Phala Network announced a strategic cooperation with Mind Network, combining TEE (Trusted Execution Environment) with FHE to create the next-generation secure zero-trust AI Agent solution. Phala Network's TEE assists AI Agents in processing data and models in a cost-effective and secure manner, then loads Mind Network's FHE SDK to encrypt inference results, which are then sent to Mind Network's FHE Hub for consensus validation. Through the integration of TEE, FHE, and blockchain, end-to-end secure services and autonomous consensus capabilities are realized, effectively solving key issues such as data security, quantum resistance, and decentralized consensus.
· Swarms: In January 2025, Mind Network and Swarms announced deep cooperation, focusing on technology integration and functional expansion. In Agent development, both sides optimized Swarms to become Swarms-rust using the Rust language, enhancing programming security and concurrency efficiency, improving system performance stability. In Multi-Agent collaboration, a secure consensus mechanism was constructed using Fully Homomorphic Encryption technology to protect data privacy and intellectual property, reducing the risk of information leakage and achieving efficient collaboration. The actual effects are significant, and the Multi-Agent system has greatly improved its ability to handle complex tasks in fields such as financial analysis and medical diagnosis, providing strong support for related work. Swarms is an important project in the AI field developed by prodigy Kye Gomez, focusing on innovation in Agent and swarm technology research and development, achieving remarkable results in Multi-Agent orchestration architecture, providing a foundation for Agent interaction.
IV. Advantages and Challenges
Advantages:
1. Technological Innovation: Mind Network is the first project to apply FHE technology to consensus management, pioneering the industry.
2. Data security upgrade: In traditional AI Agent consensus schemes, data transmission and computation need to be decrypted, which is prone to leakage. Mind Network uses FHE to keep data encrypted throughout, effectively preventing leaks in financial data processing scenarios.
3. Significant efficiency improvement: Traditional consensus algorithms reduce efficiency as the number of nodes increases. Mind Network combines FHE technology to quickly verify and reach consensus on encrypted data in large-scale AI Agent collaborations, such as intelligent urban traffic management.
4. Trust Mechanism Innovation: Traditional trust establishment relies on identity authentication and reputation mechanisms, which can be easily attacked. Mind Network, based on FHE, enables nodes to only verify encrypted data without needing to know real identities and content, solving the trust dilemma in cross-organizational supply chains.
5. The architecture is flexible and scalable: Its distributed network nodes are diverse and can be expanded on demand in the fields of DePIN and AI Agent.
6. Incentive mechanisms are improved: By utilizing native tokens, transaction fee sharing, honor rewards, etc., it stimulates nodes' participation in consensus and honesty, promoting ecological prosperity.
Challenges:
1. Performance Bottleneck: FHE technology is computationally complex, making it slow to handle large-scale data and complex tasks, with large ciphertext sizes causing difficulties in data transmission and storage.
2. Insufficient market awareness: FHE technology is relatively new, and companies and developers have limited understanding of it, with some enterprises adopting a cautious attitude.
3. Ecological construction needs to be strengthened: Currently, the Mind Network application scenarios are limited, and the ecosystem construction needs to be further improved.
V. Conclusion
Mind Network has launched airdrop queries as of April 6, allowing active participants in the testnet, mainnet, invitation events, and community contributors to receive rewards.
Although FHE technology still faces technical development bottlenecks, its future potential is enormous with technological innovation. The ecosystem built by Mind Network helps promote the overall development of the AI Agent field. For developers and ecosystem participants, opportunities and challenges coexist. It is hoped that more projects like this, which solidly build infrastructure, will emerge to collectively advance AI ecosystem construction.