gFHE - GPU fully homomorphic encryption: an optimized Python librar - Borseti, Renato José Policani (2024)
Homomorphic cryptography allows the processing of encrypted data without the need to decrypt it nor to know the secret key, which allows data privacy. This thesis presents an optimized RLWE-based library of the FHE system initially proposed by Cheon, Kim, Kim, and Song (CKKS). The lattice-based mathematical primitives need a considerable time for processing. Our library has mathematical and computational optimization based on parallel processing on GPUs to work around this problem. Due to this implementation, we obtained a speedup of 968× compared to the exact implementation in the library HElib using parallel processing on the CPU and a reduction of 87.68% in the serial execution time of a metamodel in both libraries