Vitalik预言的技术新风暴：FHE崛起重塑加密世界

Introduction

The advantages of fully homomorphic encryption: Compared with traditional encryption algorithms, its unique feature is that a third party can decrypt the encryption without decrypting it. Data can be calculated and manipulated any number of times, providing new possibilities for privacy computing.

Definition of FHE

Fully homomorphic encryption (Homomorphic Encryption, referred to as FHE): allows specific forms of algebraic operations to be performed on ciphertext, and the result is still encrypted, and the decrypted result The result is consistent with performing the same operation on plaintext. Compared with zero-knowledge proof, the biggest advantage of fully homomorphic encryption is that it gives the cloud the ability to perform calculations on encrypted data, thereby protecting sensitive information from third-party access.

Fully Homomorphic Encryption (FHE) can be separated and understood:

• The HE in FHE represents homomorphic encryption technology, and its core feature is to allow ciphertext to be Perform calculations and operations, and these operations can be directly mapped to the plain text, that is, the mathematical properties of the encrypted data remain unchanged;

• The F in FHE means that this homomorphism reaches takes a whole new level, allowing unlimited calculations and operations on encrypted data.

Comparison of FHE, ZK and MPC

In the privacy track, the three technologies at the forefront of industry technology are: FHE, ZK and MPC.

Fully Homomorphic Encryption (FHE) can perform various operations on encrypted data without decrypting it first, so that the privacy of the data is extremely protected. At the same time, FHE provides strong security guarantees for areas such as cloud computing and blockchain.

Zero-knowledge proof (ZK) is an advanced cryptography technology that plays a key role in protecting data privacy and ensuring factual correctness. Through ZK, one party can prove the authenticity of a certain statement to another party without revealing the specific data related to the statement, thus effectively protecting the privacy of the data subject. Especially in building blockchain scaling solutions, ZK is widely used, such as zk-rollups.

Multi-party computation (MPC) is a computing model based on cryptography technology, which can protect the private data of participants and complete computing tasks without exposing private inputs. MPC technology decomposes the calculation process into multiple steps and introduces encryption and decryption operations in each step, thereby enabling multiple parties to participate in the calculation without leaking private information.

It can be seen from the above comparison that FHE technology focuses on calculations without decrypting the data, thereby protecting the privacy of the data; ZK technology focuses on proving the correctness of the statement while protecting the privacy of the statement; MPC technology is committed to realizing multi-party secure computing and ensuring the privacy and security of participants during the computing process.

Importance of FHE

Better protection of privacy and security: FHE ensures the privacy and security of data during processing and calculation by encrypting data, thereby preventing data leaks and attacks . This encryption method uses mathematical principles and cryptography technology to make it possible to perform secure calculations in a cloud computing environment. During the calculation process, no one, including the data processor, can view the original content of the data, so as to avoid exposing the original content. Purpose of data.

Have more usage scenarios: FHE can be applied to secure data processing in the financial field, privacy protection in the medical field, secure cloud computing, electronic voting, secure data transmission in the Internet of Things and other fields. Through FHE technology, various industries can achieve secure processing and transmission of data, ensure the security of user privacy information, and promote the digital and intelligent development of various industries. Therefore, FHE has a wider range of application scenarios than ZK and MPC in both Web 2 and Web 3.

Key projects in the FHE field

Zama

Zama is a project focusing on fully homomorphic encryption technology.

This project focuses on developing and promoting FHE solutions to protect data privacy in the fields of blockchain and artificial intelligence. Fully homomorphic encryption is Zama's core technology, which allows arbitrary calculations to be performed on encrypted data without decryption, ensuring the privacy of data during processing. Zama provides a powerful set of open source FHE libraries and solutions that enable anyone from independent developers to large enterprises to build end-to-end encrypted applications without knowing anything about cryptography to get started.

Zama’s products and services are primarily targeted at industries such as healthcare, financial services, advertising, defense, biometrics and government security. Through its technology, Zama is able to provide privacy-preserving machine learning and smart contract solutions to these industries. In addition, Zama is actively involved in various cooperation projects to further promote the application of its FHE technology. For example, it collaborated with Mind Network to integrate its Concrete ML solution into Mind Network’s FHE verification network, setting a new standard for decentralized AI verification. Cooperate with Privasea to jointly explore the fields of AI, data security and ML, and develop a series of privacy-protecting AI applications based on the ZAMA-ConcreteML platform.

Zama has completed a $73 million Series A round of financing, led by Multicoin Capital and Protocol Labs, with Metaplanet, Blockchange Ventures, Vsquared Ventures and Stake Capital also participating.

Fhenix

Fhenix是一个基于以太坊的Layer 2解决方案，通过FHE Rollups和FHE Coprocessors提供支持。

Fhenix完全兼容以太坊虚拟机（EVM），并且对Solidity语言提供全面支持，能够运行基于FHE的智能合约，并实现链上保密计算。与其他方案不同的是，Fhenix不使用zkFHE，而是采用了Optimistic Rollup而非ZK Rollup的方式，同时利用Zama的FHE技术，通过fhEVM实现链上保密性，并专注于TFHE（Threshold FHE）技术的研发和应用。TFHE技术可以在多方参与的情况下实现全同态加密，为保护用户隐私和数据安全提供了更加可靠的解决方案。Fhenix的推出将为以太坊生态系统带来更多隐私保护和安全性，并推动区块链技术在更多领域的应用和发展。

2024 年 4 月 2 日，Fhenix 宣布将与 EigenLayer 合作开发 FHE 协处理器，希望将 FHE 引入智能合约。所谓“FHE 协处理器”，其工作重点是无需先解密信息即可对加密数据进行计算，无需在以太坊、L2 或 L3 上处理 FHE 计算任务，而是由指定的处理器处理。FHE 协处理器将受到 Fhenix 的 FHE Rollup 和 EigenLayer 质押机制的保护。按照路线图，Fhenix 计划于 2025 年 1 月上线主网。

2023 年 9 月，Fhenix 完成 700 万美元种子轮融资，Sora Ventures、Multicoin Capital 和 Collider Ventures 领投，Node Capital、Bankless、HackVC、TaneLabs 和 Metaplanet 等参投。Fhenix项目通过结合全同态加密技术和以太坊L2解决方案，为区块链领域带来了创新的保密计算能力，并且在多个领域展现出广阔的应用潜力。

Secret network

Secret Network是一个致力于隐私的区块链项目，旨在为去中心化应用（DApps）提供隐私保护。该项目允许开发者构建新型的、无权限、可保留隐私的应用程序。

Secret Network是使用Cosmos SDK和Tendermint BFT构建的Layer1区块链，是以隐私为中心的智能合约平台。它是第一个在主网上提供私密智能合约的项目。该项目通过集成Intel SGX（软件保护扩展）技术，增强了其隐私保护能力。Secret Network一开始的名字为Enigma，最初希望依托以太坊生态进行开发，但后来由于性能瓶颈，改为通过Cosmos SDK开发一条独立的支持隐私计算的公链。这条链不仅支持隐私计算，还能够实现与其他Cosmos生态系统的互操作性，将私密性带入广泛的区块链网络。

Secret Network的核心技术创新在于其集成的Intel SGX，这使得它能够在保持区块链透明度的同时，为用户提供数据隐私。Secret Network通过其独特的隐私保护功能，为Web 3.0应用程序提供了数据隐私，推动了去中心化金融等领域的发展。

Sunscreen

Sunscreen 是一家专注于隐私保护的区块链项目，致力于为工程师提供使用 FHE 等密码技术构建和部署私有应用程序的解决方案。

公司已经开源了自己的 FHE 编译器，这是一个基于 Web3 的原生编译器，能够将普通的 Rust 函数转换为具有隐私性的 FHE 等效函数，为算术操作（如 DeFi）提供高性能而无需硬件加速。此外，FHE 编译器还支持 BFV FHE 方案。同时，Sunscreen 正在着手构建与 FHE 编译器兼容的 ZKP 编译器，以确保计算完整性，尽管在证明同态运算时整体速度较慢。另外，公司也在寻求一种去中心化存储系统，用于存储 FHE 密文。

在未来的路线图规划中，Sunscreen 将首先支持测试网中的私有交易，随后支持预先确定的私有程序，并最终允许开发者使用其 FHE 与 ZKP 编译器编写任意私有程序。

2022 年 7 月，Sunscreen 完成了 465 万美元的种子轮融资，由 Polychain Capital 领投，Northzone、Coinbase Ventures、dao5 等也参与了投资，个人投资者包括 Naval Ravikan、Entropy 创始人 Tux Pacific 等。Sunscreen 的联合创始人包括 Ravital Solomon 和隐私网络 NuCypher 的联合创始人 MacLane Wilkison，公司旨在为工程师提供便利，使其能够构建基于全同态加密的应用。此前，Sunscreen 曾获得 57 万美元的 Pre-Seed 轮融资。

Mind network

Mind Network 是一种由 Zama 支持的再质押层，其目标是实现HTTPZ（端到端加密互联网愿景）。

The network’s products include MindLayer, the FHE restaking scheme for AI and DePIN networks, MindSAP, the FHE-authorized stealth address protocol, and MindLake, the FHE DataLake created based on the FHE validator network. Users can re-stake LST tokens of BTC and ETH to the Mind Network through MindLayer, and the FHE enhanced validator is introduced to achieve an end-to-end encrypted verification and calculation process. At the same time, it introduces a Proof of Intelligence (PoI) consensus mechanism specifically designed for AI machine learning tasks to ensure fair and secure distribution among FHE validators. FHE calculations can also be accelerated by hardware. MindLake is a data storage rollup for on-chain encrypted data computation.

In addition, Mind Network is launching Rollup chain together with AltLayer, EigenDA, and Arbitrum Orbit. Mind Network’s testnet has been launched. In June 2023, Mind Network completed a $2.5 million seed round from investors including Binance Labs, Comma3 Ventures, SevenX Ventures, HashKey Capital, Big Brain Holdings, Arweave SCP Ventures, Mandala Capital, and others. At the same time, it was selected for Binance Labs’ fifth season incubation program, was selected for the Chainlink BUILD program, and received the Ethereum Foundation Fellowship Grant.

Privasea

Privasea is a distributed computing network project that integrates fully homomorphic encryption machine learning (FHEML). It also launched the DApp "ImHuman" based on FHE technology to ensure that " Secure execution of Face Verification” (PoH).

Once a user creates an ImHuman account, they cannot retrieve their password if they forget it. ImHuman will use the front camera to scan the face image and encrypt it on the mobile phone. It will not be sent to any server, and Privasea does not have permission to access it. The encrypted face image will be sent to the Privasea server and used to generate a personal NFT to complete face verification. Users who pass PoH verification will receive exclusive airdrops. Currently, ImHuman is only released on Google Play and will soon be available on the App Store.

Privasea has also established the AI ​​DePIN infrastructure Privasea AI Network, and the test network has been launched. By establishing a decentralized computing network, the test network provides scalable distributed computing resources for FHE AI tasks, thereby reducing the risk of centralized data processing. Privasea's FHE solution is powered by Zama's specific machine learning. As of March 2024, Privasea has completed a $5 million seed round of financing, with investors including Binance Labs, Gate Labs, MH Ventures, K300, QB Ventures, CryptoTimes, etc. In April, Privasea completed a new round of strategic financing, with investors including OKX Ventures, Tanelabs, an incubator in which SoftBank has a stake, and others.

Risks of the FHE track

FHE is less efficient: In the current blockchain industry, due to limitations in computing power and algorithms, ZK technology is very difficult to implement. . The computing power required by FHE is 4-5 orders of magnitude greater than that of ZK (about 1000-10000 times), so it is very difficult to fully implement FHE at this stage. At this stage, only the addition and subtraction calculations of FHE can be realized, but this still requires a large amount of calculations, which will lead to relatively low calculation efficiency and require a large amount of computing power, and the cost will also increase significantly.

The market demand for FHE is not strong: Although the adoption of FHE can solve the problems faced by some industries, it is difficult and costly to implement based on FHE, which leads to projects that are willing to adopt FHE. less. And for most users, privacy is a trivial need. As a public service, few people are willing to pay a premium for privacy. The market demand for FHE is not strong, which leads to the fact that the willingness of various project parties to develop FHE is not very strong. Therefore, FHE has been in a stagnant development stage in recent years and has no real application.

Weak computing power infrastructure: The basic premise of being able to realize FHE is that a large amount of computing power is required. FHE addition calculations have proven that the CPU cannot meet the most basic computing needs of FHE. It must be GPUs and ASICs are just enough. But now the world is in a stage of computing power shortage due to the rise of the AI ​​industry. Nvidia's GPUs have been scheduled to be produced until 2025, and the decentralized computing power project in the Crypto industry is due to the lack of total computing power and bandwidth. Problems with hardware equipment such as TPS and TPS do not meet the conditions for developing FHE. In this context of computing power shortage, it is unrealistic to develop the FHE track on a large scale.

Summary

First of all, FHE, as the Holy Grail of cryptography, can use its unique algorithm to enable third parties to perform any number of calculations and operations on encrypted data without decrypting it. Offering new possibilities for private computing. FHE technology can effectively protect user data privacy while achieving secure sharing and processing of data. Not only in the Crypto industry, but also in all walks of life in real society, it can also play an innovative role and solve existing privacy issues for all walks of life.

其次，FHE作为一个早期的赛道，其面临困难也比较多。FHE的效率受限于当前区块链行业中算力和算法的限制，使得FHE技术实现难度重重。尽管FHE能够解决部分行业问题，但其所需的计算能力大约是ZK的1000-10000倍，因此目前只能实现FHE的加法和减法计算，其应用受到市场需求不高和算力基础设施薄弱的影响，使得FHE的发展停滞不前。

总体来说，FHE是一个非常具有前景和开创性的赛道，FHE技术能够有效保护用户数据隐私，同时实现数据的安全共享和处理。但是FEH因为基础设施的受限以及因为效率和成本问题导致的市场需求程度的不高在实现的过程中困难重重。所以FHE未来Crypto行业发展的一个方向但是在现阶段仍处于其早期的阶段并不具备其项目应用落地的条件。

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