The FHE concept is popular among VCs. What projects are worth paying attention to?
Original author: Poopman
Translation: Joyce, BlockBeats
Editor's note:
"FHE" is a hot technical topic in the crypto community recently.
Two weeks ago, Ethereum Layer 2 Fhenix announced the completion of a $15 million Series A financing led by Hack VC. As early as last year, Fhenix had received a seed round of financing led by Multicoin. Fhenix is an Ethereum L2 powered by FHE Rollups and FHE Coprocessors, which can run FHE-based smart contracts with on-chain confidential computing. Yesterday, Sam Williams, the founder of Arweave, which is undergoing a major update, also posted on social media that the function of using FHE for privacy computing within the AO process will be launched soon.
There are many FHE ecological projects. This long article by community KOL Poopman has made a basic review of the concept of FHE and ecological projects, and proposed the technical challenges and possible solutions faced by FHE. BlockBeats compiled it as follows:
FHE opens the possibility of computing encrypted data without decryption. When combined with blockchain, MPC, and ZKP (scalability), FHE provides the necessary confidentiality and supports a variety of on-chain use cases.
In this article, I will introduce four issues, namely the background of FHE, how FHE works, 5 landscapes of the FHE ecosystem, and current challenges and solutions of FHE.
Background of FHE
FHE was first proposed in 1978, but due to its computational complexity, it was not practical for quite a long time and was quite theoretical. It wasn’t until 2009 that Craig developed a working model for FHE, sparking research interest in FHE.
In 2020, Zama launched TFHE and fhEVM, bringing FHE to the forefront of the cryptocurrency space. Since then, we’ve seen the emergence of general EVM-compatible FHE L1/L2 (e.g., Fhenix, Inco, and FHE compilers (e.g., Sunscreen, etc.)
How does FHE work?
You can imagine a blind box with a puzzle inside. However, the blind box cannot know anything about the puzzle you gave it, but it can still calculate the result mathematically.
If that’s too abstract, you can learn more from my simplistic explanation of FHE. FHE is a privacy technology that allows computations to be performed on encrypted data without decrypting it first. In other words, any third party or cloud can process sensitive information without accessing any data inside.
So what are the use cases for FHE? Enhanced privacy for machine learning, cloud computing, 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 ERC20, private voting, NFT blind auctions, more secure MPC, front-running protection, trustless bridges.
FHE Ecosystem
In general, the prospects of on-chain FHE can be summarized into 5 areas. They are general FHE, FHE/HE for specific use cases (applications), FHE accelerated hardware, FHE Wif AI, and "alternative solutions".
General FHE Blockchain and Tools
They are the backbone of blockchain confidentiality. This includes SDK, coprocessor, compiler, new execution environment, blockchain, FHE module... The most challenging one is to introduce FHE to EVM, namely fhEVM.
fhEVM:
Zama ( @zama_fhe ), as the representative of fhEVM - the first provider to provide TFHE (Fully Homomorphic Encryption) + fhEVM (Fully Homomorphic Virtual Machine) solutions.
Fhenix ( @FhenixIO ), implements FHE L2 (second layer) + FHE coprocessor on ETH.
Inco network ( @inconetwork ), EVM-compatible FHE L1 focused on gaming/RWA (real world assets)/DID (decentralized identity)/social.
FairMath ( @FairMath ), a fully homomorphic virtual machine (FHE-(E)VM) research organization working with openFHE to promote the implementation and adoption of FHE.
FHE infrastructure tools:
Octra network ( @octra ), a blockchain supporting HFHE (high-order fully homomorphic encryption) isolated execution environments.
Sunscreen ( @SunscreenTech ), a fully homomorphic compiler based on Rust, relying on Microsoft's SEAL library.
Fairblock ( @0xfairblock ), a provider of programmable encryption and conditional decryption services, also supports tFHE (threshold fully homomorphic encryption).
Dero ( @DeroProject ), L1 with HE (homomorphic encryption) support, for private transactions (not FHE).
Arcium ( @ArciumHQ ), developed by the @elusivprivacy team, combines HE (homomorphic encryption) + MPC (multi-party computation) + ZK (zero-knowledge proof) privacy L1.
Shibraum FHE chain, FHE L1 made with zama TFHE solution.
FHE/HE for specific use cases
Penumbrazone ( @penumbrazone ): A cross-chain Cosmos dex (appchain) that uses tFHE as its shielded exchange/pool.
zkHold-em ( @zkHoldem ): Is a poker game on Manta that uses HE and ZKP to prove the fairness of the game.
Hardware Accelerated FHE
Whenever FHE is used for intensive computations like FHE-ML, bootstrapping to reduce noise growth is critical. Solutions like hardware acceleration play an important role in facilitating bootstrapping, with ASICs performing best.
Optalysys (@Optalysys), a hardware company focused on accelerating all TEE-related software, including FHE, through optical computing.
Chain Reaction (@chainreactioni0), a hardware company that makes chips that help make mining more efficient. They plan to launch a FHE chip by the end of 2024.
Ingonyama ( @Ingo_zk ) is a semiconductor company focusing on ZKP/FHE hardware acceleration. Existing products include ZPU.
Cysic ( @cysic_xyz ) is a hardware acceleration company. Its existing products include self-developed FPGA hardware, as well as the upcoming ZK DePiN chip, ZK Air and ZK Pro.
Each company specializes in producing hardware such as chips, ASICs, and semiconductors that can accelerate the boot/computation of FHE.
AI X FHE
Recently, there has been a growing interest in integrating FHE into AI/ML. Among them, FHE can prevent machines from learning any sensitive information while processing it and provide confidentiality for data, models, and outputs throughout the process.
Ai x FHE members include:
Mind network( @mindnetwork_xyz ), a FHE re-staking layer for securing Proof of Stake (PoS) and AI networks through high-value data encryption and private voting, reducing the chance of node collusion and manipulation.
SightAl( @theSightAI ), a verifiable FHE AI inference blockchain with verifiable FHE-ML. The blockchain consists of three main parts: the Sight Chain, the Data Aggregation Layer (DA Layer), and a Sight Inference Network, where FHE-ML tasks are performed.
Based AI( @getbasedai ), Based AI is an L1 blockchain that integrates FHE with large language models (LLMs) using a mechanism called "Cerberus Squeezing" that can convert any LLM into an encrypted zero-knowledge large language model (ZL-LLM).
Privasea Al( @Privasea_ai ), Privasea AI is an AI network that allows users to encrypt their data or models using the FHE scheme in the HESea library and then upload them to the Privasea-AI network, where the blockchain processes the data in an encrypted state.
The HESea library is comprehensive, contains different libraries for TFHE, CKKS, and BGV/BFV, and is compatible with a range of schemes.
「Alternative Solutions」MPC/ZKFHE
Some do not use FHE, but use MPC to protect high-value data and perform "blind computations", while others use ZKSNARKs to guarantee the correctness of FHE computations on encrypted data. They are:
Nillion Network ( @nillionnetwork ), a computing network that uses MPC to decentralize and store high-value data, while allowing users to write programs and perform blind computations. Nillion consists of two main components: the coordination layer and the Petnet. The coordination layer acts as a payment channel, while the Petnet performs blind computations and storage of high-value data.
Padolabs ( @padolabs ), Pado is a computing network that uses FHE to process sensitive data, while using MPC-TLS and ZKP to ensure the correctness of the calculation.
Challenges and solutions of FHE
Unlike ZK and MPC, FHE is still in its early stages. What is the bottleneck of FHE now? In order to enhance the security of the calculation, some "noise" is added to the ciphertext during encryption. When too much "noise" accumulates in the ciphertext, it becomes too "noisy" and eventually affects the accuracy of the output. Different solutions are exploring how to effectively eliminate noise without imposing too many restrictions on the design, including TFHE, CKKS, BGV, etc.
The main challenges of FHE include:
Slow performance:Currently, private smart contracts using fh-EVM have only 5 TPS. TFHE is now about 1000x slower than pure data.
Not yet suitable for developers:There is still a lack of standardized algorithms and overall support for FHE tools.
High computational overhead (cost): This can lead to node centralization due to noise management and complex computations for bootstrapping.
Risks of FHE on unsecured chains: To secure any threshold decryption system, the decryption keys are distributed among the nodes. However, due to the high overhead of FHE, this can lead to a low number of validators and therefore a high probability of collusion.
Solutions include:
Programmable boost: It allows computations to be applied during bootstrapping, thereby increasing efficiency while being application specific.
Hardware acceleration: ASICs, GPUs, and FPGAs are developed along with the OpenFHE library to accelerate FHE performance.
Better threshold decryption systems. In short, in order to make on-chain FHE more secure, we need a system (can be MPC) to ensure: low latency; lower node entry barriers and achieve decentralization; fault tolerance.
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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.
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