Original Author: Poopman
Translated by: Joyce, BlockBeats Editor’s Note:
“FHE” is a hot topic in the crypto community recently.
Just two weeks ago, Ethereum Layer 2 Fhenix announced the completion of a $15 million Series A funding led by Hack VC, and as early as last year, Fhenix had already received seed funding led by Multicoin. Fhenix is an Ethereum L2 supported by FHE Rollups and FHE Coprocessors, capable of running smart contracts with on-chain confidential computing based on FHE. Yesterday, Sam Williams, the founder of Arweave, who is undergoing significant updates, also posted on social media, indicating the upcoming launch of privacy computing in the AO process using FHE.
With numerous FHE ecosystem projects, community KOL Poopman’s comprehensive article provides a basic overview of FHE’s concepts and ecosystem projects, while also presenting the technical challenges and potential solutions facing FHE. The translation by BlockBeats is as follows:
FHE unlocks the possibility of computing encrypted data without decryption. When combined with blockchain, MPC, and ZKP (scalability), FHE provides the necessary confidentiality and supports various on-chain use cases.
In this article, I will introduce four issues: the background of FHE, how FHE works, the five landscapes of the FHE ecosystem, and the current challenges and solutions for FHE.
The Background of FHE
FHE was first proposed in 1978, but due to its computational complexity, it was impractical for quite some time and remained highly theoretical. It wasn’t until 2009 that Craig developed a viable model for FHE, sparking interest in research on FHE.
In 2020, Zama introduced TFHE and fhEVM, making FHE a focal point in the cryptocurrency field. Since then, we have seen the emergence of general EVM-compatible FHE L1/L2 (such as Fhenix, Inco, and FHE compilers like Sunscreen).
How FHE Works?
You can imagine a blind box with a puzzle inside. However, the blind box has no knowledge of any information about the puzzle you give it, yet it can still compute the mathematical result.
If that’s too abstract, you can learn more from my simplified explanation of FHE. FHE is a privacy technology that allows computation on encrypted data without decryption. In other words, any third party or cloud can process sensitive information without accessing any internal data.
So what are the use cases for FHE? Enhancing privacy in machine learning, cloud computing, and on-chain gambling through ZKP and MPC. Private on-chain transactions/private smart contracts/privacy-focused virtual machines, such as FHEVM, etc.
Some FHE use cases include private on-chain computation, on-chain data encryption, private smart contracts on public networks, confidential ERC 20, private voting, NFT blind auctions, more secure MPC, front-running protection, and trustless bridges.
The FHE Ecosystem
Overall, the prospects for on-chain FHE can be summarized into five areas: general FHE, FHE/HE for specific use cases (applications), FHE acceleration hardware, FHE with AI, and “Alternative Solutions”.
General FHE Blockchain and Tools
They are the cornerstone of achieving confidentiality on the blockchain. This includes SDKs, coprocessors, compilers, new execution environments, blockchains, FHE modules… The most challenging aspect is integrating FHE into EVM, namely fhEVM.
fhEVM:
Zama (@zama_fhe), as the representative of fhEVM – the first provider of TFHE (fully homomorphic encryption) + fhEVM (fully homomorphic virtual machine) solution.
Fhenix (@FhenixIO), implementing FHE L2 (second layer) + FHE coprocessor on ETH.
Inco network (@inconetwork), focusing on EVM-compatible FHE L1 in the gaming/RWA (real-world assets)/DID (decentralized identity)/social fields.
FairMath (@FairMath), a research organization collaborating with openFHE, dedicated to promoting the implementation and adoption of FHE.
FHE Infrastructure Tools:
Octra network (@octra), supporting a blockchain with HFHE (high-order fully homomorphic encryption) isolated execution environment.
Sunscreen (@SunscreenTech), a Rust-based fully homomorphic compiler that relies on Microsoft’s SEAL library.
Fairblock (@0x fairblock), a provider of programmable encryption and conditional decryption services, also supporting tFHE (threshold fully homomorphic encryption).
Dero (@DeroProject), supporting HE (homomorphic encryption) on L1 for private transactions (not FHE).
Arcium (@ArciumHQ), an L1 developed by the @elusivprivacy team combining HE (homomorphic encryption), MPC (multi-party computation), and ZK (zero-knowledge proof) for privacy.
Shibraum FHE chain, an FHE L1 made using the Zama TFHE solution.
FHE/HE for Specific Use Cases
Penumbrazone (@penumbrazone): a cross-chain Cosmos dex (appchain) using tFHE for its shielded exchange/pool.
zkHold-em (@zkHoldem): a poker game on Manta, using HE and ZKP to prove the fairness of the game.
Hardware Accelerated FHE
When using FHE for intensive computations like FHE-ML, reducing noise growth is crucial to guide performance. Solutions like hardware acceleration play a vital role in facilitating guidance, with ASICs offering optimal performance.
Optalysys (@Optalysys), a hardware company focusing on accelerating all TEE-related software through optical computing, including FHE.
Chain Reaction (@chainreactioni 0), a hardware company producing chips to improve mining efficiency. They plan to release an FHE chip by the end of 2024.
Ingonyama (@Ingo_zk), a semiconductor company specializing in ZKP/FHE hardware acceleration. Current products include ZPU.
Cysic (@cysic_xyz) is a hardware acceleration company with
