FHE: The Future of Privacy Computing

Fully Homomorphic Encryption ( FHE ) is an advanced encryption technology that allows for computation directly on encrypted data, enabling data processing under privacy protection. FHE has broad application prospects in various fields such as finance, healthcare, and cloud computing, but currently faces issues such as high computational overhead and poor scalability, making it a while before large-scale commercial use is realized.

Basic Principles of FHE

The core of FHE is to encrypt the plaintext information using polynomial encryption and perform calculations in the encrypted state. The specific steps include:

1. Select the key polynomial
2. Generate random polynomial
3. Add a small "error" polynomial as noise
4. Use the above polynomial to encrypt the original text.

Decryption requires a secret key polynomial to ensure security. However, multiple computations can lead to noise accumulation, affecting decryption. To address this issue, FHE employs the following key technologies:

• Key switch: Compress ciphertext size
• Modulus switching: control noise growth
• Bootstrap: Reset noise to initial level

These technologies enable FHE to support arbitrary function computation of unlimited depth, achieving true fully homomorphic encryption.

Challenges Facing FHE

The biggest challenge of FHE is the enormous computational overhead, which is millions of times slower than ordinary computation. To address this issue, the U.S. DARPA has launched the Dprive program, aiming to increase the speed of FHE computation to 1/10 of ordinary computation. The main research directions include:

• Increase the processor word length
• Develop dedicated ASIC processors
• Build MIMD parallel architecture

Although progress is slow, FHE still has enormous potential in the field of privacy-preserving computation, especially in the processing of sensitive data such as military and medical information.

The Application of FHE in Blockchain

FHE is mainly used in the blockchain field to protect data privacy, including:

• On-chain privacy transactions
• AI training data privacy protection
• On-chain privacy voting
• MEV Protection

However, FHE also brings some problems, such as reduced network throughput and increased node operating costs.

Main FHE Projects

• Zama: Provides a complete development stack based on the TFHE scheme.
• Octra: Implements bootstrap using hypergraphs technology
• Fhenix: Building a privacy-first Layer 2 network
• Privasea: Focused on LLM data computation
• Mind Network: Combining Restaking Model

Outlook

FHE technology is still in its early stages and faces many challenges. However, with the development of dedicated chips and more capital entering the field, FHE is expected to bring profound changes in areas such as defense, finance, and healthcare. The deployment of FHE chips will be a key step towards commercialization and is worth ongoing attention.