Advantages of fully homomorphic encryption: Compared with traditional encryption algorithms, its unique feature is that a third party can perform any number of calculations and operations on encrypted data without decrypting it, providing new possibilities for privacy computing.
Definition of FHE
Homomorphic Encryption (FHE): allows a specific form of algebraic operations to be performed on ciphertext, and the result obtained is still encrypted. The decrypted result is consistent with the result of the same operation on the 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 broken down into:
The HE in FHE stands for homomorphic encryption technology. Its core feature is that it allows calculations and operations on ciphertext, and these operations can be directly mapped to plaintext, that is, the mathematical properties of the encrypted data are kept unchanged;
The F in FHE represents a whole new level of homomorphism, allowing unlimited computations and operations on encrypted data.
Comparison of FHE with 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, thus highly protecting the privacy of the data. At the same time, FHE provides strong security guarantees for fields such as cloud computing and blockchain.
Zero-knowledge proof (ZK) is an advanced cryptographic technology that plays a key role in protecting data privacy and ensuring the correctness of facts. Through ZK, one party can prove the authenticity of a statement to another party without revealing the specific data related to the statement, thereby effectively protecting the privacy of the data subject. ZK is widely used in building blockchain expansion solutions, such as zk-rollups.
Multi-party computation (MPC) is a computing model based on cryptography technology that can protect the privacy data of the participants and complete computing tasks without exposing private inputs. MPC technology breaks down the computing process into multiple steps and introduces encryption and decryption operations in each step, thereby enabling multiple parties to participate in computing without leaking private information.
From the above comparison, it can be seen that FHE technology focuses on performing calculations without decrypting data, thereby protecting the privacy of data; ZK technology focuses on proving the correctness of statements while protecting the privacy of statements; MPC technology is committed to achieving multi-party secure computing to ensure the privacy and security of participants during the computing process.
The Importance of FHE
Better protection of privacy and security: FHE ensures the privacy and security of data during the computing process by encrypting the data, thereby preventing data leakage and attacks. This encryption method uses mathematical principles and cryptographic techniques to make secure computing possible in a cloud computing environment. During the computing process, no one, including the data processor, can view the original content of the data, thereby achieving the purpose of not exposing the original data.
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 many other fields. Through FHE technology, all walks of life can achieve secure data processing and transmission, ensure the security of user privacy information, and promote the digitalization 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 field of FHE
Zama
Zama is a project focused on fully homomorphic encryption technology.
The project is committed to developing and promoting FHE solutions to protect data privacy in the fields of blockchain and artificial intelligence. Fully homomorphic encryption is the core technology of Zama, which allows arbitrary computations on encrypted data without decryption, thereby ensuring the privacy of data during processing. Zama provides a powerful set of open source FHE libraries and solutions, allowing everyone from independent developers to large enterprises to build end-to-end encrypted applications without having to know anything about cryptography to get started.
Zamas products and services are mainly aimed 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 collaborations to further promote the application of its FHE technology. For example, it cooperated with Mind Network to integrate its Concrete ML solution into Mind Networks FHE verification network to set a new standard for decentralized AI verification. It also cooperated with Privasea to jointly explore the fields of AI, data security, and ML, and develop a series of privacy-preserving AI applications based on the ZAMA-ConcreteML platform.
Zama has completed a $73 million Series A funding round led by Multicoin Capital and Protocol Labs, with participation from Metaplanet, Blockchange Ventures, Vsquared Ventures, and Stake Capital.
Fhenix
Fhenix is a Layer 2 solution based on Ethereum, powered by FHE Rollups and FHE Coprocessors.
Fhenix is fully compatible with the Ethereum Virtual Machine (EVM) and provides full support for the Solidity language. It can run FHE-based smart contracts and implement on-chain confidential computing. Unlike other solutions, Fhenix does not use zkFHE, but adopts Optimistic Rollup instead of ZK Rollup. At the same time, it uses Zamas FHE technology to achieve on-chain confidentiality through fhEVM, and focuses on the research and development and application of TFHE (Threshold FHE) technology. TFHE technology can achieve fully homomorphic encryption with the participation of multiple parties, providing a more reliable solution for protecting user privacy and data security. The launch of Fhenix will bring more privacy protection and security to the Ethereum ecosystem, and promote the application and development of blockchain technology in more fields.
On April 2, 2024, Fhenix announced that it will work with EigenLayer to develop an FHE coprocessor, hoping to introduce FHE into smart contracts. The so-called FHE coprocessor focuses on computing encrypted data without decrypting the information first. FHE computing tasks do not need to be processed on Ethereum, L2 or L3, but by designated processors. The FHE coprocessor will be protected by Fhenixs FHE Rollup and EigenLayer staking mechanisms. According to the roadmap, Fhenix plans to launch the mainnet in January 2025.
In September 2023, Fhenix completed a $7 million seed round of financing, led by Sora Ventures, Multicoin Capital and Collider Ventures, and participated by Node Capital, Bankless, HackVC, TaneLabs and Metaplanet. The Fhenix project brings innovative confidential computing capabilities to the blockchain field by combining fully homomorphic encryption technology and Ethereum L2 solutions, and has shown broad application potential in multiple fields.
Secret network
Secret Network is a privacy-focused blockchain project that aims to provide privacy protection for decentralized applications (DApps). The project allows developers to build new types of permissionless, privacy-preserving applications.
Secret Network is a Layer 1 blockchain built with Cosmos SDK and Tendermint BFT. It is a privacy-centric smart contract platform. It is the first project to provide private smart contracts on the mainnet. The project has enhanced its privacy protection capabilities by integrating Intel SGX (Software Guard Extensions) technology. Secret Network was originally named Enigma. It initially hoped to develop based on the Ethereum ecosystem, but later due to performance bottlenecks, it changed to develop an independent public chain that supports privacy computing through Cosmos SDK. This chain not only supports privacy computing, but also enables interoperability with other Cosmos ecosystems, bringing privacy to a wide range of blockchain networks.
The core technological innovation of Secret Network lies in its integrated Intel SGX, which enables it to provide data privacy to users while maintaining blockchain transparency. Through its unique privacy protection capabilities, Secret Network provides data privacy for Web 3.0 applications, driving the development of areas such as decentralized finance.
Sunscreen
Sunscreen is a privacy-focused blockchain project dedicated to providing engineers with solutions for building and deploying private applications using cryptographic techniques such as FHE.
The company has open-sourced its own FHE compiler, a native Web3-based compiler that converts ordinary Rust functions into privacy-preserving FHE equivalents, providing high performance for arithmetic operations (such as DeFi) without hardware acceleration. In addition, the FHE compiler also supports the BFV FHE scheme. At the same time, Sunscreen is working on building a ZKP compiler compatible with the FHE compiler to ensure computational integrity, although the overall speed is slower when proving homomorphic operations. In addition, the company is also seeking a decentralized storage system for storing FHE ciphertexts.
In the future roadmap, Sunscreen will first support private transactions in the testnet, then support pre-determined private programs, and finally allow developers to write arbitrary private programs using its FHE and ZKP compilers.
In July 2022, Sunscreen completed a $4.65 million seed round of financing, led by Polychain Capital, with participation from Northzone, Coinbase Ventures, dao 5, and others. Individual investors include Naval Ravikan, Entropy founder Tux Pacific, and others. Sunscreens co-founders include Ravital Solomon and MacLane Wilkison, co-founder of the privacy network NuCypher. The company aims to facilitate engineers to build applications based on fully homomorphic encryption. Previously, Sunscreen received $570,000 in Pre-Seed rounds of financing.
Mind network
Mind Network is a Zama-backed re-staking layer that aims to realize HTTPZ, the vision for an end-to-end encrypted internet.
The networks products include MindLayer, an FHE re-staking solution for AI and DePIN networks, MindSAP, an FHE-authorized stealth address protocol, and MindLake, an FHE DataLake created based on the FHE validator network. Users can re-stake LST tokens of BTC and ETH to Mind Network through MindLayer, and introduce FHE enhanced validators to achieve end-to-end encrypted verification and calculation processes. At the same time, it introduces a proof-of-intelligence (PoI) consensus mechanism designed specifically 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 calculations. In addition, Mind Network is launching a Rollup chain with AltLayer, EigenDA, and Arbitrum Orbit. Mind Networks testnet is already online. In June 2023, Mind Network completed a $2.5 million seed round of financing, with investors including Binance Labs, Comma 3 Ventures, SevenX Ventures, HashKey Capital, Big Brain Holdings, Arweave SCP Ventures, Mandala Capital, etc. 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 encrypted machine learning (FHEML). It also launched the DApp ImHuman based on FHE technology to ensure the secure execution of face verification (PoH).
Once a user creates an ImHuman account, they will not be able to retrieve it if they forget their password. ImHuman will use the front camera to scan facial images and encrypt them on the phone. They will not be sent to any server, and Privasea will not have access to them. The encrypted facial image will be sent to the Privasea server and used to generate a personal NFT to complete facial verification. Users who pass the PoH verification will receive an exclusive airdrop. Currently, ImHuman is only available on Google Play and will soon be available on the App Store. Privasea has also established the AI DePIN infrastructure Privasea AI Network, which has been launched as a test network. 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. Privaseas FHE solution is supported by Zamas 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, K 300, QB Ventures, CryptoTimes, etc. In April, Privasea completed a new round of strategic financing, with investors including OKX Ventures and Tanelabs, an incubator in which SoftBank has a stake.
Risks of the FHE Track
FHE is inefficient: In the current blockchain industry, due to the limitations of 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 addition and subtraction calculations of FHE can be implemented, but this still requires a large amount of calculations, which will lead to low efficiency of calculations, 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, the difficulty and cost of implementing FHE are relatively high, resulting in fewer projects willing to adopt FHE. In addition, privacy is a painless demand for most users. As a public service, few people are willing to pay for the privacy premium. 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 no real application has been implemented.
Weak computing power infrastructure: The basic premise for realizing FHE is that a large amount of computing power is required. The fact that FHE addition calculations are done has proven that CPUs cannot meet the most basic computing requirements of FHE. GPUs and ASICs are required to meet them. However, due to the rise of the AI industry, the world is currently in a computing power shortage phase. Nvidias GPUs have been scheduled for production until 2025, and decentralized computing power projects in the Crypto industry do not have the conditions to develop FHE due to insufficient total computing power and hardware equipment issues such as bandwidth and TPS. In the context of this computing power shortage, it is unrealistic to want to develop the FHE track on a large scale.
Summarize
First of all, FHE, as the holy grail of cryptography, can enable third parties to perform any number of calculations and operations on encrypted data without decryption through its unique algorithm, providing new possibilities for privacy computing. FHE technology can effectively protect user data privacy while achieving secure data sharing and processing. Not only in the Crypto industry, but also in all walks of life in real society, it can play an innovative role and solve existing privacy problems for all walks of life.
Secondly, as an early track, FHE faces many difficulties. The efficiency of FHE is limited by the computing power and algorithm limitations in the current blockchain industry, making the implementation of FHE technology difficult. Although FHE can solve some industry problems, the computing power required is about 1000-10000 times that of ZK. Therefore, FHE can only realize addition and subtraction calculations. Its application is affected by low market demand and weak computing power infrastructure, which makes the development of FHE stagnant.
In general, FHE is a very promising and groundbreaking track. FHE technology can effectively protect user data privacy while achieving secure data sharing and processing. However, FEH faces many difficulties in its implementation due to limited infrastructure and low market demand caused by efficiency and cost issues. Therefore, FHE is a direction for the future development of the Crypto industry, but at this stage it is still in its early stages and does not have the conditions for its project application.
