FHE: The Future of Privacy Computing

Fully Homomorphic Encryption ( FHE ) is an advanced encryption technology that allows direct computation on encrypted data, enabling data processing while protecting privacy. FHE has potential applications in various fields such as finance, healthcare, and cloud computing, but its commercialization still requires time, with the main challenges being its enormous computational and memory overhead.

The basic principle of FHE is to use polynomials to hide the original information. The encryption process introduces random polynomials and small "error" polynomials, and only by knowing the key can the plaintext be restored. To achieve computations of arbitrary depth, FHE uses techniques such as key switching, modulus switching, and bootstrapping to manage noise.

The biggest problem currently facing FHE is computational efficiency, which is millions of times slower than ordinary computation. The U.S. Department of Defense's Advanced Research Projects Agency ( DARPA ) is promoting related research, with the goal of increasing FHE computation speed to 1/10 of that of ordinary computation. The main areas of improvement include increasing the processor word length, developing dedicated ASIC chips, and building parallel computing architectures.

In the blockchain field, FHE can be used to protect on-chain privacy, AI training data privacy, and more. Some projects like Zama and Fhenix are exploring the application of FHE in blockchain. However, the high computational cost of FHE also brings new challenges; finding a balance between privacy protection and efficiency is a dilemma.

Overall, FHE is a forward-looking technology that is still in its early stages. With the development of specialized hardware and increased investment, FHE is expected to bring profound changes in fields such as defense, finance, and healthcare. Although the technological challenges remain significant, FHE, as a key technology for privacy computing, has a promising future.