Author: Poopman, Ouro Research; Translation: 0xjs@黄金财经
FHE opens the possibility of computing on encrypted data without decryption.
When combined with blockchain, MPC, ZKP (scalability), FHE provides the necessary confidentiality and supports a variety of on-chain use cases.
This article briefly outlines the current status of FHE.
I will introduce: 1. FHE background; 2. How does FHE work? 3. 5 areas in the FHE ecosystem; 4. Current FHE challenges and solutions.
FHE was first proposed in 1978, but due to its high computational complexity, it was not practical for a long time and remained only at the theoretical level.
It was not until 2009 that Craig developed a feasible model for FHE, and since then, research interest in FHE has soared.
In 2020, Zama launched TFHE and fhEVM, making FHE the focus of attention in the encryption field.
Since then, we have seen the emergence of general EVMs compatible with FHE L1/L2 such as Fhenix, Inco Network, and FHE compiler Sunscreen Tech.
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.
Some use cases of FHE include: private on-chain computing, 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, etc.
Overall, the landscape of on-chain FHE can be summarized into 5 areas: 1. General FHE; 2. FHE/HE for specific use cases (applications); 3. FHE acceleration hardware; 4. FHE with AI; 5. Alternative solutions
They are the backbone to achieve blockchain confidentiality. This includes SDK, coprocessor, compiler, new execution environment, blockchain, FHE module...
The most challenging one is: introducing FHE into EVM, namely fhEVM.
Including:
fhEVM: Zama, Fhenix, Inco network, Fair Math
FHE tools/infrastructure: Octra, Sunscreen Tech, Fairblock, Dero, Arcium (formerly Elusiv), Shibarium
Below is a one-sentence summary of each project.
Penum: Cross-chain cosmos dex (appchain) using tFHE for shielded exchange/pool.
zkHoldem: Poker game on Manta Network, using HE and ZKP to prove the fairness of the game.
Whenever FHE is used for intensive computations such as FHE-ML, guidance to reduce noise growth is critical. Solutions such as hardware acceleration play an important role in facilitating guidance, with ASICs performing best.
Members in the hardware field include: Optalysys, Chain Reaction, Ingonyama, Cysic
Each company specializes in producing products that can accelerate FHE Hardware for boot/computation, such as chips, ASICs, and semiconductors.
Recently, there has been a growing interest in integrating FHE into AI/ML. Among them, FHE can prevent machines from learning any sensitive information when processing sensitive information, and provide confidentiality for data, models, and outputs throughout the process.
Members of AI x FHE include: Mind Network, Sight AI, BasedAI, Privasea
They are: Nillion network, Pado Labs
With ZK and MPC Unlike traditional cryptographic algorithms, FHE is still in its early stages.
Slow performance: Currently, private smart contracts using fh-EVM have only 5 TPS. In addition, TFHE is now about 1000 times slower than pure data.
Not developer-friendly yet: There is still a lack of standardized algorithms and overall supported FHE tools.
High computational overhead (cost): It can lead to node centralization due to noise management and bootstrapping of complex computations.
Risks of insecure on-chain FHE: For any secure threshold decryption system, the decryption keys are distributed among nodes. However, due to the high overhead of FHE, this may lead to a reduced number of validators and therefore a higher probability of collusion.
Programmable bootstrapping: It allows computation to be applied during bootstrapping, thereby improving efficiency while being application-specific.
Hardware acceleration: ASICs, GPUs, and FPGAs are being developed together with the OpenFHE library to accelerate FHE performance.
Better threshold decryption system: In short, in order to make on-chain FHE more secure, we need a system (can be MPC) to ensure: low latency, lower decentralized entry barriers for nodes, and fault tolerance.
FHE stands for Fully Homomorphic Encryption, which allows calculations to be performed directly on encrypted data, and the calculation results obtained are consistent with the results of the same calculations on the original data.
JinseFinanceFHE fully homomorphic encryption is the next generation of privacy protection technology that is about to emerge and is worth our planning.
JinseFinancePrivasea uses FHE technology to provide data privacy and security, leveraging AI and distributed network architecture to allow complex data processing and analysis while keeping the data fully encrypted.
JinseFinanceFully homomorphic encryption (FHE) is a class of cryptographic schemes that allows computations to be performed on encrypted data (ciphertext) without decrypting the data, opening up a range of use cases for privacy and data protection.
JinseFinanceIn this article, I try to outline the development history of a cryptographic technology, which is different from the various technological changes in the crypto industry.
JinseFinanceWith the continuous evolution and rapid development of the blockchain industry, a series of cutting-edge cryptographic technologies have emerged, among which zero-knowledge proof (ZKP), multi-party computation (MPC) and fully homomorphic encryption (FHE) are the most prominent.
JinseFinanceIntroducing Fully Homomorphic Encryption (FHE): Explore its exciting applications, limitations, and recent developments that have fueled its popularity.
JinseFinanceFHE solution Zama raised US$73 million in Series A financing. The CEO talked about Zama’s history, progress, current situation and overall plan.
JinseFinanceJinseFinanceJinseFinance