FHE: The Key Technology for Future Privacy Computing

FHE (Fully Homomorphic Encryption) is an advanced encryption technology that enables computation on encrypted data, allowing for data processing while protecting privacy. FHE has multiple potential application scenarios, especially in areas that require privacy-preserving data processing and analysis, such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. However, its commercialization still requires time, with the main challenges being the significant computational and memory overhead brought by the algorithms and poor scalability.

Basic Principles of FHE

The core of FHE is to use polynomials to hide the original information. The simplified FHE encryption process is as follows:

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

During decryption, as long as you know s(x), you can recover m from c(x). Introducing randomness and errors can enhance security.

To support computation on encrypted data, FHE transforms operations into "circuits". However, each operation increases noise, and when the noise becomes too large, it cannot be decrypted correctly. To address this issue, FHE employs the following key technologies:

• Key switching: Compressing ciphertext size
• Modulus switching: Control noise growth
• Bootstrap: Reset noise to the initial level

Currently, mainstream FHE schemes all use Bootstrap technology, but the computational overhead is still quite large.

Challenges Facing FHE

The main challenge of FHE lies in its enormous computational overhead. Compared to ordinary computation, the computation speed of FHE is approximately 1 million times slower. To improve FHE performance, the U.S. Department of Defense Advanced Research Projects Agency ( DARPA ) launched the Dprive program in 2021, aiming to increase the computation speed of FHE to 1/10 of ordinary computation. The program mainly focuses on the following aspects:

1. Increase the processor word length
2. Develop dedicated ASIC processors
3. Constructing MIMD Parallel Architecture

Despite the slow progress, FHE technology remains significant for protecting sensitive data in the long run, especially in fields such as military, healthcare, and finance.

The Application of FHE in Blockchain

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

• On-chain privacy
• AI training data privacy
• On-chain voting privacy
• On-chain privacy transaction review
• Potential Solutions to MEV Issues

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

Major FHE Projects

The main FHE projects currently include:

• Zama: provides a TFHE-based development stack
• Fhenix: Building a Privacy-First Layer 2
• Privasea: Focused on LLM data computation
• Inco Network: Build FHE Layer 1
• Arcium: Integration of multiple cryptographic technologies
• Mind Network: Restaking track

Among them, Zama is the provider of FHE technology used by most projects.

Future Outlook

FHE technology is still in its early stages and is developing more slowly compared to ZK technology. However, with increasing capital attention and the development of FHE-specific chips, FHE is expected to bring profound changes in fields such as national defense, finance, and healthcare. The deployment of FHE chips will be an important prerequisite for its commercialization.

Despite facing numerous technical challenges, FHE, as a cutting-edge technology with definite demand, still has great potential in the future. It is expected to unleash the enormous potential of combining private data with emerging technologies such as quantum algorithms, ushering in explosive development.