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From theoretical geek to computing star: the rise of fully homomorphic encryption and its future vision | Bee Network

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From theoretical geek to computing star: the rise of fully homomorphic encryption and its future vision | Bee Network Login トレンドニュース ミーム・ローンチパッド AIエージェント デサイ トップチェーンエクスプローラー 初心者向け 100x コイン ビーゲーム 重要なウェブサイト 必須のアプリ クリプトセレブリティ デピン ルーキーズ・エッセンシャル トラップディテクタ 基本的なツール 高度な Web サイト 交換 NFTツール こんにちは、 サインアウト Web3 ユニバース ゲーム ダップ ミツバチの巣 成長するプラットフォーム 広告 検索 英語 コインをリチャージする ログイン ダウンロード Web3 ユニ ゲーム ダップ ミツバチの巣 広告 ホーム-分析-本文 From theoretical geek to computing star: the rise of fully homomorphic encryption and its future vision分析1年前 (2025)更新ワイアット 32,5271 From theoretical geek to computing star: the rise of FHE and its future vision Today, $Swarms performance has attracted widespread attention. With the return of AI Agent funds, Swarms is becoming one of the top applications in the AI narrative in 2025. 市場 observers generally believe that Swarms token economic system has not yet been fully implemented, but the planning is clear and clear. Once the system is online, the adoption rate and value-added application scenarios of tokens will increase significantly. As AI quickly grows from the intersection of Web2 and Web3 to an important sector that attracts secondary funds, Swarms is expected to become a cross-industry star project in the process.

In the world of 暗号graphy, fully homomorphic encryption (FHE) is like a genius that has been buried for many years, finally coming to the center of the stage and attracting more and more attention. It is worth mentioning that the recent successful IPO 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 transition of FHE technology from academic research to commercial application.

FHEの背景 FHE was first proposed in 1978, but due to its computational complexity, it could not be put into practical use for a long time and remained in the theoretical stage. Although academic idealists praised it, it was always like an idealized theoretical nerd who could not break through the constraints of the ivory tower.

It was not until 2009 that Craig Gentry proposed a feasible FHE model, breaking the previous technical limitations and gradually transforming FHE from a high-cold theory to a technical dark horse that can be practically applied. This breakthrough is like a classmate in the class who has average grades and is lazy all the time suddenly becoming a blockbuster and 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 dont want the workers to steal the gold in the process. So, you put the gold in a closed transparent box and lock the box, and the workers can only operate it with gloves. Even if the workers can operate it, the gold cannot be taken away, and the box ensures the integrity of the gold. The box symbolizes the encryption algorithm, the lock represents the key, the worker is the operator of the encryption calculation, and the encrypted data is the gold. In this way, FHE realizes calculations in an encrypted state, which not only protects data privacy, but also performs complex computing tasks.

The beauty of FHE is that it allows computing on encrypted data without first decrypting it. Imagine that you can modify a file in a safe without opening it. For personal privacy and corporate data, FHE is undoubtedly a strong line of defense to protect data. While ensuring data privacy, it can also ensure that operations on data are not leaked and integrity is maintained.

The main application scenarios of FHE include:

Data privacy protection: In fields such as medicine and finance, the security of sensitive data is crucial. FHE can perform calculations 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 computing process.

Smart contracts: In the Web3 field, FHE enables 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 have begun to explore this field and promote the practice and development of its application. FHE is not limited to the calculation of encrypted data, but is widely used in cloud computing, Web3, AI, privacy transactions, quantum resistance and other fields. The following are some representative FHE projects:

座間 As a pioneer of FHE technology, Zama 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.

フェニックス Fhenix implements the FHE L2 (second layer) solution on Ethereum (ETH), using FHE accelerators and virtual machines (VMs) to perform data encryption calculations.

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

柴犬 Treat Recently, the Shiba Inu team has brought new value and opportunities to its ecosystem by introducing fully homomorphic encryption (FHE) technology and the functional token $Treat, attracting 12 million in financing. This innovation combines data privacy protection with blockchain technology to improve the operational efficiency of the ecosystem. While ensuring data privacy, FHE makes complex calculations possible. $Treat is not only expanding in the Web3 field, but also actively penetrating into the Web2 field, aiming to create a payment system suitable for 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 AI is invisible during the interaction with user data, avoiding privacy leaks while achieving seamless interaction with AI. Their identity verification app #ImHuman combines facial recognition technology to verify user identity, and uses FHE technology to ensure that data is always encrypted during the identity verification process.

Sunscreen Based on Rusts fully homomorphic compiler, Sunscreen is committed to providing encrypted computing capabilities for blockchain applications to help users achieve privacy protection.

Octra Network Octra Network is an isolated execution environment blockchain that supports FHE and higher-order 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 construction to specific applications, such as FHE encrypted smart contracts, private chain computing, data encryption storage, privacy-preserving transactions, etc.

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 combination of FHE and AI, especially in multi-agent systems. Multi-agent systems are a collaborative framework in which multiple AI agents work together to solve complex problems and improve 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 to achieve 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 to ensure the privacy of sensitive information. Specifically, Mind Networks FHE solution ensures the following:

Data protection: Even during the calculation process, data always remains encrypted to avoid leakage of sensitive information and ensure 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, and avoiding the leakage of any sensitive information.

Efficient collaboration: Through FHE technology, multiple agents can collaborate without exposing sensitive information, achieving efficient processing of complex tasks.

Mind Networks technology not only improves 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.

Combining Swarms with FHE: Driving AI Multi-Agent Consensus It is worth mentioning that the latest progress in FHE and multi-agent collaboration has also been officially supported by Swarms. The Swarms team is actively promoting the capabilities of AI agents and Swarm systems, especially in the combination of Rust programming language and FHE-powered consensus schemes. 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 Rust, aiming to provide more efficient and reliable cross-platform application development. Its particular advantage is the ability to securely exchange information between multiple agents and achieve encrypted consensus through FHE technology. The features of this project include:

AI consensus: Multiple agents reach consistent decisions through encrypted data aggregation and consensus mechanisms while ensuring the security of models and data.

Cross-agent collaboration: Enable secure and encrypted data exchange between multiple agents to ensure the privacy of information.

Autonomy: Support decentralized autonomous decision-making, reduce human intervention, and achieve autonomous collaboration among intelligent agents.

The Swarms team clearly stated that FHE is one of the key technologies for achieving efficient and secure multi-agent consensus solutions, especially in protecting the intellectual property rights of agent models and ensuring the reliability of transaction decisions. For example, in the field of trading, multiple professional agents can make decisions based on their own private models, and ultimately obtain more credible results through encrypted consensus voting, thereby greatly improving the accuracy and reliability of decisions.

要約する As a technology with broad application prospects, FHE is profoundly changing the way we process data. From blockchain to AI, from cloud computing to privacy protection, FHE provides a new way to perform data calculations while ensuring privacy. As FHE technology continues to mature, more and more projects and platforms are applying it to practical scenarios, promoting the advancement and innovation of encryption technology.

In this process, Mind Network has demonstrated great potential with its leading technology in combining FHE with AI. By providing secure and efficient encryption 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 promoted the ability of multi-agent collaboration through FHE and built a more secure and efficient consensus framework. With the further development of FHE technology, the integration of AI and encryption technology will become an important development trend in the future digital world.

This article is sourced from the internet: From theoretical geek to computing star: the rise of fully homomorphic encryption and its future vision

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