FHE: A New Frontier in Data Privacy Protection

FHE( fully homomorphic encryption ) is an advanced encryption technology that allows direct computation on encrypted data, thereby processing data while protecting privacy. FHE has potential applications in various fields such as finance, healthcare, and cloud computing, but due to the enormous computational overhead, commercialization will still take time.

Basic Principles of FHE

The core of FHE is using polynomials to hide the original data. A simplified FHE system may include:

• Choose a key polynomial s(x)
• Generate a random polynomial a(x)
• Generate a small "noise" polynomial e(x)
• Encrypted plaintext m: c(x) = m + a(x)*s(x) + e(x)

During decryption, it is sufficient to know s(x) to recover the plaintext. The introduction of noise is intended to enhance security, but it also poses challenges - the noise accumulates with computation and may ultimately lead to incorrect decryption.

To solve the noise problem, FHE employs several techniques:

• Key switch: compress ciphertext size
• Modulus switching: reduce noise budget
• Bootstrap: Resetting the noise to the initial level

Currently, mainstream FHE schemes are based on bootstrapping technology, but the computational overhead is still quite large.

Challenges Facing FHE

The main challenge of FHE lies in its enormous computational overhead. It is estimated that FHE computations are about 1 million times slower than regular computations. To address this issue, the U.S. Department of Defense's Advanced Research Projects Agency (DARPA) launched the DPRIVE program, aiming to increase the speed of FHE computations to 1/10 of that of regular computations.

The DPRIVE plan mainly focuses on the following aspects:

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

Although progress is slow, FHE technology is still of great importance for protecting sensitive data, especially in the post-quantum era.

The Application of FHE in Blockchain

In the field of blockchain, FHE is mainly used to protect data privacy, with applications including:

• On-chain privacy protection
• AI Training Data Privacy
• On-chain voting privacy
• MEV mitigation

However, FHE also faces challenges such as reduced network throughput and increased node operating requirements.

Main FHE Projects

The main FHE projects currently include:

• Zama: Provides a complete development stack based on the TFHE scheme
• Fhenix: Building a Privacy-First Optimism Layer 2
• Privasea: Focused on LLM data computation
• Inco Network: Building FHE Layer 1
• Mind Network: Combining Restaking Model
• Octra: Implements FHE using unique hypergraphs technology.

Outlook

FHE technology is still in its early stages and faces many challenges. However, with the development of dedicated chips and more capital investment, FHE is expected to bring profound changes in fields such as defense, finance, and healthcare. Although the commercialization path is long, FHE has great potential for future development as an important tool for privacy protection.