Paper 2023/1467
GPU Acceleration of High-Precision Homomorphic Computation Utilizing Redundant Representation
Abstract
Fully homomorphic encryption (FHE) can perform computations on encrypted data, allowing us to analyze sensitive data without losing its security. The main issue for FHE is its lower performance, especially for high-precision computations, compared to calculations on plaintext data. Making FHE viable for practical use requires both algorithmic improvements and hardware acceleration. Recently, Klemsa and Önen (CODASPY'22) presented fast homomorphic algorithms for high-precision integers, including addition, multiplication and some fundamental functions, by utilizing a technique called redundant representation. Their algorithms were applied on TFHE, which was proposed by Chillotti et al. (Asiacrypt'16). In this paper, we further accelerate this method by extending their algorithms to multithreaded environments. The experimental results show that our approach performs 128-bit addition in 0.41 seconds, 32-bit multiplication in 4.3 seconds, and 128-bit Max and ReLU functions in 1.4 seconds using a Tesla V100S server.
Metadata
- Available format(s)
- Category
- Implementation
- Publication info
- Published elsewhere. WAHC 2023 – 11th Workshop on Encrypted Computing & Applied Homomorphic Cryptography
- DOI
- 10.1145/3605759.3625256
- Keywords
- FHEredundant binaryGPU acceleration
- Contact author(s)
-
sh-narisada @ kddi com
ir-okada @ kddi com
ka-fukushima @ kddi com
sh-kiyomoto @ kddi com
nishide @ risk tsukuba ac jp - History
- 2023-09-28: last of 2 revisions
- 2023-09-25: received
- See all versions
- Short URL
- https://ia.cr/2023/1467
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1467, author = {Shintaro Narisada and Hiroki Okada and Kazuhide Fukushima and Shinsaku Kiyomoto and Takashi Nishide}, title = {{GPU} Acceleration of High-Precision Homomorphic Computation Utilizing Redundant Representation}, howpublished = {Cryptology {ePrint} Archive, Paper 2023/1467}, year = {2023}, doi = {10.1145/3605759.3625256}, url = {https://eprint.iacr.org/2023/1467} }