FHE: The Future Path of Privacy Computing

Fully Homomorphic Encryption ( FHE ) is an advanced encryption technology that allows for 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 due to its significant computational overhead, it is still some way from commercial implementation.

The basic principle of FHE is to convert plaintext data into polynomial form for encryption, and then perform computational operations on the ciphertext. To control noise growth, FHE employs techniques such as key switching, modulus switching, and bootstrapping. Currently, mainstream FHE schemes include BGV, BFV, CKKS, etc.

The main challenge facing FHE is computational efficiency, which is about a million times slower than conventional computing. To address this, the U.S. DARPA launched the DPRIVE program, aimed at increasing the computational speed of FHE to 1/10th that of conventional computing. This program focuses on optimizing FHE performance through increasing processor word length, developing dedicated ASIC chips, and building parallel architectures.

In the blockchain field, FHE can be used to protect on-chain privacy, AI training data privacy, privacy voting, and other scenarios. Currently, several projects are exploring the combination of FHE and blockchain, such as Zama, Fhenix, and Octra. Among them, Zama has built a relatively complete development toolchain based on TFHE, while Octra has proposed a new type of FHE implementation based on hypergraphs.

Although FHE is still in its early stages, its future prospects are broad with the development of related hardware and increased funding. FHE is expected to bring profound changes to industries such as defense, finance, and healthcare, unlocking the immense potential of privacy data.