FHE: The Future Star of Privacy Computing

FHE( fully homomorphic encryption ) is an advanced encryption technology that allows computations to be performed directly on encrypted data, enabling data processing under privacy protection. This technology has broad application prospects in multiple fields such as finance, healthcare, and cloud computing, but it still faces challenges such as high computational overhead and poor scalability, and it is still some distance away from large-scale commercial use.

Basic Principles of FHE

The core idea of FHE is to hide the original information through polynomials and convert the computation into circuit form. The specific steps include:

1. Choose the key polynomial and the random polynomial
2. Generate small "error" polynomials
3. Encrypt the plaintext
4. Convert the calculation process into a circuit

In this process, noise is introduced to ensure security, but the noise accumulates as the number of computations increases, which may ultimately lead to the inability to recover the original text. To address this issue, several techniques have been proposed:

• Key switching: compress ciphertext but introduce a small amount of noise
• Modulus switching: reduce noise budget
• Bootstrap: reset noise to initial level

Currently, mainstream FHE schemes adopt Bootstrap technology, but the computational overhead is still significant.

Challenges Facing FHE

The biggest problem with FHE is its extremely low computational efficiency. Compared to ordinary computation, the computational overhead of FHE can be hundreds of millions of times higher. To address this, DARPA launched the DPRIVE program, aiming to increase the computation speed of FHE to 1/10 of ordinary computation. The main focus is on the following aspects:

1. Increase processor word length
2. Develop dedicated ASIC processors
3. Adopting MIMD parallel architecture

However, the plan is progressing slowly, and there is still a significant gap to the target.

The Application of FHE in Blockchain

FHE is primarily used in the blockchain field to protect data privacy, with application scenarios including:

• On-chain privacy protection
• AI training data privacy
• On-chain voting privacy
• On-chain transaction review

FHE is also regarded as one of the potential solutions to the MEV problem. However, due to efficiency issues, its current application in blockchain is still limited.

Main Project Introduction

The main projects in the current FHE field include:

• Zama: Provides a TFHE-based development toolchain
• Fhenix: Building a privacy-first Layer 2
• Privasea: Focused on LLM data computation
• Inco Network: Layer 1 built on fhEVM
• Arcium: integrates technologies such as FHE, MPC, and ZK
• Mind Network: Provides security in conjunction with Restaking

Zama, as an infrastructure provider, has provided technical support for multiple projects.

Future Outlook

Although FHE technology is still in its early stages and faces many challenges, its potential for privacy protection is immense. With more funding and talent投入, as well as the development of dedicated chips, FHE is expected to bring profound changes in fields such as defense, finance, and healthcare. As the era of quantum computing approaches, the importance of FHE technology will become even more pronounced.