From "Theoretical Otaku" to "Computational Star": The Rise and Future Prospects of FHE

Today, the performance of $Swarms has attracted widespread attention. With the influx of AI Agent capital, $Swarms is becoming one of the leading applications in the AI narrative for 2025. Market observers generally believe that the token economic system of $Swarms has not yet been fully implemented, but the plan is clear and explicit. Once the system is launched, the adoption rate and value-added application scenarios of the token will increase significantly. As AI rapidly grows from the intersection of Web2 and Web3 into an important sector attracting secondary capital, $Swarms is expected to become a cross-industry star project 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 center stage and attracting more and more attention. It is worth mentioning that the recent successful token sale of the Shiba Treat project has brought additional attention to the FHE track. By combining Fully Homomorphic Encryption with decentralized applications, Shiba Treat has attracted the attention of a large number of investors and developers, marking a new stage in the commercialization of FHE technology.

Background of FHE

FHE was first proposed in 1978, but due to its computational complexity, it was unable to be practically applied for a long time and remained in the theoretical stage. Although the academic idealists praised it highly, it was always like an idealized "theoretical otaku" and could not break free from the constraints of the ivory tower.

Until 2009, Craig Gentry proposed a feasible FHE model, breaking through the previous technical limitations and transforming FHE from a "high-cold theory" into a "technical dark horse" that can be practically applied. This breakthrough was like a classmate who was not outstanding in grades and lived a lazy life suddenly becoming a sensation, 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 analogy: Suppose you have a piece of gold that needs to be processed, but you don't want the workers to steal the gold during the process. So you put the gold in a sealed transparent box, lock it, and the workers can only operate through gloves. Even if the workers can operate, the gold cannot be taken away, and the box ensures the integrity of the gold. This box represents the encryption algorithm, the lock represents the key, the workers are the operators of the encrypted computation, and the encrypted data is the gold. In this way, FHE achieves computation in an encrypted state, ensuring data privacy while executing complex computational tasks.

The charm of FHE lies in the fact that it allows computation to be performed in an encrypted state without the need for decryption first. Imagine that you can modify files in a safe without opening it and still be able to operate. For personal privacy and corporate data, FHE is undoubtedly a powerful defense for data protection. It not only ensures data privacy, but also ensures that the operation on the data is not leaked and the integrity is maintained.

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 the privacy of data during the computation process.

• Smart contracts: In the Web3 domain, FHE allows smart contracts to execute contract content and manage digital assets while ensuring privacy.

FHE Ecosystem: From Infrastructure to Application Projects

With the continuous development of FHE technology, more and more projects are exploring this field, promoting the practical application and development of FHE. FHE is not only limited to the computation of encrypted data, but is widely used in cloud computing, Web3, AI, privacy transactions, quantum resistance, and other fields. Here are some representative FHE projects:

Zama

As a pioneer of FHE technology, Zama has launched TFHE and fhEVM, making FHE a focus in the cryptocurrency field. By providing a fully homomorphic encryption solution, 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), using FHE accelerators and virtual machines (VMs) to achieve encrypted data computation.

Mind Network

Focused on providing privacy protection solutions for decentralized AI applications through fully homomorphic encryption technology. The platform uses FHE encryption methods to enable AI algorithms to be trained and inferred while protecting user data privacy, making the computation and analysis of sensitive data secure and transparent. Mind Network not only performs computations on encrypted data, but also promotes the development of AI applications in a decentralized framework.

Shiba Inu Treat

Recently, the Shiba Inu team has introduced Fully Homomorphic Encryption (FHE) technology, coupled with the utility token $Treat, to bring 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 build a payment system applicable to the real world, and may become a tool for cross-border payments in the future.

Privasea AI

Privasea AI uses FHE technology to ensure that the interaction between AI and user data is "invisible", avoiding privacy leaks while achieving seamless interaction with AI. They have launched the identity verification application #ImHuman, which combines facial recognition technology to verify user identity and uses FHE technology to ensure that the data in the identity verification process is always in an encrypted state.

Sunscreen

Sunscreen, a Rust-based fully homomorphic compiler, is dedicated to providing encrypted computation capabilities for blockchain applications, helping users achieve privacy protection.

Octra Network

Octra Network supports an isolated execution environment blockchain with FHE and higher-order fully homomorphic encryption (HFHE), focusing on enhancing data privacy and security.

These projects demonstrate the diverse application scenarios of FHE technology, covering various fields from infrastructure building to specific applications, such as FHE encrypted smart contracts, private chain computation, encrypted data storage, and privacy-preserving transactions.

The Future of FHE and AI Collaboration

Among the many application fields 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 and AI, especially in the application of multi-agent systems. Multi-agent systems are a collaborative framework in which multiple AI agents work together to solve complex problems, improving efficiency through cooperation. However, ensuring that data is not leaked during the computation process while maintaining trust and cooperation between agents remains a major challenge in achieving this goal.

Mind Network provides a secure and efficient solution for multi-agent systems through FHE. In this solution, all data is kept in an encrypted state during processing, ensuring the privacy of sensitive information is not compromised. Specifically, Mind Network's FHE solution ensures the following:

• Data protection: Even during the computation process, data remains in an encrypted state, avoiding the leakage of sensitive information and ensuring 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, without leaking any 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 between AI agents. For example, in financial analysis applications, Mind Network uses FHE to ensure that data is encrypted throughout the entire process, protecting the privacy and security of sensitive data.

The Combination of Swarms and FHE: Driving Consensus for AI Multi-Agent Systems

It is worth noting that the latest developments in FHE and multi-agent collaboration have also received the support of the Swarms official, and the Swarms team is actively promoting the enhancement of the capabilities of AI agents and Swarm systems, especially in the integration 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 their 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 a more efficient and reliable cross-platform application development. Its particular advantage lies in its ability to securely exchange information between multiple agents and perform encrypted consensus through FHE technology. The features of this project include:

• AI Consensus: Multiple agents reach a consensus decision through encrypted data aggregation and consensus mechanisms, while ensuring the security of models and data.

• Cross-Agent Collaboration: Achieve secure and encrypted data exchange between multiple agents, ensuring the privacy of information.

• Autonomy: Support decentralized autonomous decision-making, reduce manual intervention, and enable autonomous collaboration between agents.

The Swarms team clearly stated that FHE is one of the key technologies for realizing an efficient and secure multi-agent consensus solution, especially in protecting the intellectual property of agent models and ensuring the reliability of transaction decisions. For example, in the field of transactions, multiple professional agents can make decisions based on their own private models, and ultimately reach a more reliable result through encrypted consensus voting, significantly improving the accuracy and reliability of decision-making.

Summary

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 approach to data computation while ensuring privacy. As FHE technology continues to mature, more and more projects and platforms are applying it to real-world scenarios, driving the progress and innovation of encryption technology.

In this process, Mind Network, with its leading technology in the integration of FHE and AI, has shown great 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. At the same time, the Swarms team has further enhanced 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 development trend in the future digital world.