FHE: The Future Path of Privacy Computing

Fully Homomorphic Encryption ( FHE ) is an advanced encryption technology that allows computations to be performed directly on encrypted data. This means that sensitive information can be processed while preserving privacy. FHE has potential applications in various fields such as finance, healthcare, and cloud computing, but currently faces the challenge of significant computational overhead.

Basic Principles of FHE

The core idea of FHE is to use polynomials to hide the original data. For example, the encrypted number 2 might:

1. Choose a key polynomial s(x)
2. Generate a random polynomial a(x)
3. Add a small "noise" polynomial e(x)
4. Encrypted result: c(x) = 2 + a(x)*s(x) + e(x)

During decryption, as long as you know the key s(x), you can recover the original data 2 from c(x).

The main challenge facing FHE is noise growth. Each computation increases the noise, which may eventually lead to incorrect decryption. To address this, researchers have proposed several techniques:

• Key Switching: Compress Ciphertext Size
• Modulus switching: reduce noise
• Bootstrap: Resetting the noise to the initial level

Currently, mainstream FHE schemes all adopt bootstrapping technology, but the computational overhead is still very high.

Issues Facing FHE

The overhead of FHE computation is several orders of magnitude higher than that of ordinary computation. According to estimates from the U.S. Department of Defense Advanced Research Projects Agency ( DARPA ), FHE computation is about 1 million times slower than ordinary computation.

To accelerate FHE, DARPA has launched the DPRIVE program, focusing on the following aspects:

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

However, the plan is progressing slowly, and there is still a gap from the expected target.

The Application of FHE in Blockchain

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

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

However, FHE also faces challenges in efficiency and computational resource requirements.

Main Projects

The main projects in the field of FHE currently include:

• Zama: Based on the TFHE scheme, providing a complete development toolchain.
• Fhenix: Building a privacy-first Layer 2 network
• Privasea: Focused on LLM data privacy protection
• Inco Network: Building FHE Layer 1 Network
• Arcium: Integrating technologies such as FHE, MPC, and ZK
• Mind Network: Combining Restaking and FHE subnet architecture
• Octra: Implements FHE using unique hypergraphs technology

Future Outlook

FHE technology is still in its early stages and faces many challenges:

1. Low efficiency, huge computational overhead
2. The difficulty of project implementation is high
3. The commercial prospects are unclear.
4. Lack of capital investment

However, with the development of dedicated chips and the influx of more funds, FHE is expected to bring about transformation in fields such as defense, finance, and healthcare, unleashing the potential of private data. The future development of FHE is worth looking forward to.