From "Theoretical Nerd" to "Computational Star": The Rise of FHE and Future Prospects
Today, the performance of $Swarms has attracted widespread attention. With the return of funds to AI Agents, Swarms is becoming one of the leading applications in the AI narrative of 2025. Market observers generally believe that the token economic system of Swarms has not yet fully materialized, but the planning is clear and defined. Once the system goes live, the adoption rate of the token and the value-added application scenarios will significantly increase. As AI rapidly grows from the intersection of Web2 and Web3 into an important sector attracting secondary funding, Swarms is expected to become a star project across industries in this process.
In the world of cryptography, Fully Homomorphic Encryption (FHE) is like a genius who has been buried for years, finally stepping into the spotlight and attracting more and more attention. Notably, the recent successful fundraising activity of the Shiba Treat project has brought additional focus to the FHE track. By combining fully homomorphic encryption with decentralized applications, Shiba Treat has captured the attention of a large number of investors and developers, marking a new phase of commercialization for FHE technology moving from academic research.
Background of FHE
FHE was first proposed in 1978, but due to its computational complexity, it could not be practically applied for a long time and remained in the theoretical stage. Idealists in academia praised it, but it always resembled an idealized "theoretical nerd," unable to break free from the confines of the ivory tower.
It wasn't until 2009 that Craig Gentry proposed a feasible FHE model, breaking previous technological limitations and gradually transforming FHE from a "high-cold theory" into a "technical dark horse" that could be practically applied. This breakthrough was like that student in class who was not outstanding academically and lived lazily suddenly shining brightly, becoming the new darling of the scientific community.
Breakthroughs and Applications of FHE Technology
The working principle of FHE can be understood through a vivid metaphor: suppose you have a piece of gold that needs to be processed, but you do not want the workers to steal the gold during the process. So, you place the gold in a sealed transparent box and lock it, allowing the workers to operate only through gloves. Even if the workers can operate, the gold still cannot be taken away, as the box ensures the integrity of the gold. This box symbolizes the encryption algorithm, the lock represents the key, the workers are the operators of encrypted computation, and the encrypted data is the gold. In this way, FHE enables computation in an encrypted state, ensuring data privacy while executing complex computational tasks.
The charm of FHE lies in its ability to perform computations while the data is encrypted, without needing to decrypt it first. Imagine being able to modify a file while it is in a safe, without needing to open it, yet still being able to operate on it. For personal privacy and corporate data, FHE is undoubtedly a powerful line of defense for data protection. It ensures that data privacy is maintained while also ensuring that operations on the data are not leaked, preserving integrity.
The main application scenarios of FHE include:
Data privacy protection: In fields such as healthcare and finance, the security of sensitive data is crucial, and FHE can perform computations without exposing the data.
Cloud computing and big data: Data processing often occurs in the cloud, and FHE can ensure that the privacy of data is not compromised during computation.
Smart contracts: In the Web3 domain, FHE enables smart contracts to execute contract content and manage digital assets while ensuring privacy.
FHE Ecosystem: From Infrastructure to Application Projects
As FHE technology continues to develop, more and more projects are beginning to explore this field, promoting its practical application and development. FHE is not limited to the computation of encrypted data but is widely applied in cloud computing, Web3, AI, privacy trading, quantum resistance, and other fields. Here are some representative FHE projects:
Zama
As a pioneer in FHE technology, Zama has launched TFHE and fhEVM, making FHE a focal point in the cryptocurrency field. By providing fully homomorphic encryption solutions, Zama has realized the application of FHE on EVM (Ethereum Virtual Machine) compatible blockchains.
Fhenix
Fhenix has implemented an FHE L2 (Layer 2) solution on Ethereum (ETH), utilizing FHE accelerators and virtual machines (VM) to achieve encrypted data computation.
Mind Network
Focusing on providing privacy protection solutions for decentralized AI applications through fully homomorphic encryption technology. The platform employs FHE encryption methods to enable AI algorithms to train and infer while protecting user data privacy, making the computation and analysis of sensitive data secure and transparent. Mind Network not only computes on encrypted data but also promotes the application development of AI in a decentralized framework.
Shiba Inu Treat
Recently, the Shiba Inu team introduced fully homomorphic encryption (FHE) technology, combined with the functional token $Treat, bringing new value and opportunities to its ecosystem, attracting $12 million in funding. This innovation combines data privacy protection with blockchain technology, enhancing the operational efficiency of the ecosystem. While ensuring data privacy, FHE makes complex computations possible. $Treat not only expands in the Web3 domain but also actively penetrates the Web2 domain, aiming to create a payment system suitable for the real world, which may become a tool for cross-border payments in the future.
Privasea AI
Privasea AI utilizes FHE technology to ensure that AI interacts with user data in an "invisible" manner, avoiding privacy leaks while achieving seamless interaction with AI. Their identity verification application #ImHuman combines facial recognition technology to verify user identity and uses FHE technology to ensure that data remains encrypted throughout the identity verification process.
Sunscreen
A fully homomorphic compiler based on Rust, Sunscreen is dedicated to providing encrypted computing capabilities for blockchain applications, helping users achieve privacy protection.
Octra Network
Octra Network supports FHE and High-order Fully Homomorphic Encryption (HFHE) in an isolated execution environment blockchain, focusing on enhancing data privacy and security.
These projects showcase the diverse application scenarios of FHE technology, covering various fields from infrastructure construction to specific applications, such as FHE encrypted smart contracts, private chain computation, data encrypted storage, and privacy-protecting transactions.
The Future of FHE and AI Collaboration
Among the many application areas of FHE technology, AI and Multi-Agent Systems (MAS) are one of the most promising directions. Mind Network is actively promoting the integration of FHE with AI, especially in the application of multi-agent systems. Multi-agent systems are a collaborative framework where multiple AI agents work together to solve complex problems, improving efficiency through cooperation. However, ensuring that data is not leaked during computation while maintaining trust and collaboration among agents remains a significant challenge to achieving this goal.
Mind Network provides a secure and efficient solution for multi-agent systems through FHE. In this solution, all data remains encrypted during processing, ensuring that sensitive information is not leaked. Specifically, Mind Network's FHE solution ensures the following:
Data protection: Even during computation, data remains encrypted, preventing sensitive information leaks and safeguarding data privacy.
Secure consensus: AI agents submit encrypted results, and the FHE network verifies the accuracy and consistency of these results, ensuring that the final consensus is both secure and reliable, avoiding any leakage of sensitive information.
Efficient collaboration: Through FHE technology, multiple agents can collaborate without exposing sensitive information, achieving efficient processing of complex tasks.
Mind Network's technology not only enhances the security and privacy protection capabilities of multi-agent systems but also promotes efficient collaboration among AI agents. For example, in financial analysis applications, Mind Network ensures that data is encrypted throughout the entire process, safeguarding the privacy and security of sensitive data.
The Combination of Swarms and FHE: Promoting AI Multi-Agent Consensus
It is worth mentioning that the latest progress in FHE and multi-agent collaboration has also received support from the Swarms team, which is actively promoting the enhancement of AI agents and the Swarm system's capabilities, especially in the combination of the Rust programming language and FHE-powered consensus solutions. By adopting FHE technology, Swarms is building an encrypted computing consensus framework that allows multiple agents to collaborate without exposing data.
Specifically, the Swarms-rust project is a multi-agent orchestration platform re-implemented by the Swarms team in the Rust language, aiming to provide more efficient and reliable cross-platform application development. Its particular advantage lies in the ability to securely exchange information among multiple agents and achieve encrypted consensus through FHE technology. The project's features include:
AI consensus: Multiple agents reach consensus decisions through encrypted data aggregation and consensus mechanisms while ensuring the security of models and data.
Cross-agent collaboration: Secure, encrypted data exchange among multiple agents ensures the privacy of information.
Autonomy: Supports decentralized autonomous decision-making, reducing human intervention and achieving autonomous collaboration among agents.
The Swarms team clearly states that FHE is one of the key technologies for achieving efficient and secure multi-agent consensus solutions, especially in protecting the intellectual property of agent models and ensuring the reliability of transaction decisions. For example, in the trading field, multiple specialized agents can make decisions based on their private models, ultimately arriving at more credible results through encrypted consensus voting, significantly improving the accuracy and reliability of decisions.
Conclusion
As a technology with broad application prospects, FHE is profoundly changing the way we handle data. From blockchain to AI, from cloud computing to privacy protection, FHE provides a new method for data computation while ensuring privacy. As FHE technology continues to mature, more projects and platforms are applying it to real-world scenarios, promoting the advancement and innovation of encryption technology.
In this process, Mind Network, with its leading technology in the integration of FHE and AI, demonstrates tremendous potential. By providing secure and efficient encrypted computing support for multi-agent systems, Mind Network not only enhances data privacy protection but also promotes innovation in AI collaboration. Meanwhile, the Swarms team is further advancing the capabilities of multi-agent collaboration through FHE, building a more secure and efficient consensus framework. As FHE technology continues to develop, the integration of AI and encryption technology will become an important trend in the future digital world.
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