FHE Technology: A New Hope for Data Privacy Protection

Fully Homomorphic Encryption ( FHE ) is an advanced encryption technology that allows for computations on encrypted data, enabling data processing while protecting privacy. FHE has potential applications in various fields such as finance, healthcare, and cloud computing, but it is still some way from large-scale commercialization, with the main challenge being its significant computational overhead.

Basic Principles of FHE

The core idea of FHE is to use polynomials to hide the original data and to perform computations on the polynomials to enable the processing of encrypted data. The specific steps include:

1. Choose a key polynomial
2. Generate Random Polynomial
3. Introduce small "error" polynomials
4. Use the above polynomial to encrypt the original data

To address the issue of noise accumulation during computation, FHE employs techniques such as key switching, modulus switching, and bootstrapping. Among these, the bootstrapping technique can reset the noise and enable computation of infinite depth, making it the core of FHE.

Currently mainstream FHE schemes include BGV, BFV, CKKS, etc., all of which use bootstrapping techniques.

Challenges Facing FHE

The biggest challenge of FHE is its extremely high computational cost, which is millions of times slower than ordinary computation. To address this, 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 ordinary computation.

DPRIVE mainly focuses on the following aspects:

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

Although progress is slow, the unique value of FHE in protecting sensitive data still makes it significant in the long term.

The Application of FHE in Blockchain

FHE can be applied to privacy protection in blockchain, including:

• On-chain privacy
• AI training data privacy
• On-chain privacy voting
• On-chain transaction privacy audit
• MEV Resistance

But FHE will also bring new challenges, such as reducing network throughput and increasing the threshold for running nodes.

Major FHE Projects

The main FHE projects currently include:

• Zama: Built a complete development stack based on the TFHE scheme.
• Fhenix: Privacy Layer 2 built on Zama
• Privasea: Using FHE for LLM data computation
• Inco Network: Building FHE Layer 1
• Arcium: Integrating FHE, MPC, and ZK technologies
• Mind Network: A Restaking network built on FHE
• Octra: Implements FHE using hypergraphs technology

Outlook

FHE is still in its early stages, facing many technical and commercialization challenges. However, with more funding and teams joining, as well as the development of dedicated chips, FHE is expected to bring transformation in fields such as defense, finance, and healthcare, unlocking the potential of private data.