Privacy concerns have thrust privacy-preserving computation into the spotlight. Homomorphic encryption (HE) is a cryptographic system that enables computation to occur directly on encrypted data, providing users with strong privacy (and security) guarantees while using the same services they enjoy today unprotected. While promising, HE has seen little adoption due to extremely high computational overheads, rendering it impractical. Homomorphic encryption (HE) is a cryptographic system that enables computation to occur directly on encrypted data. In this paper we develop a benchmark suite, named NTTSuite, to enable researchers to better address these overheads by studying the primary source of HE's slowdown: the number theoretic transform (NTT). NTTSuite constitutes seven unique NTT algorithms with support for CPUs (C++), GPUs (CUDA), and custom hardware (Catapult HLS).In addition, we propose optimizations to improve the performance of NTT running on FPGAs. We find our implementation outperforms the state-of-the-art by 30%.
@article{arxiv.2405.11353,
title = {NTTSuite: Number Theoretic Transform Benchmarks for Accelerating Encrypted Computation},
author = {Juran Ding and Yuanzhe Liu and Lingbin Sun and Brandon Reagen},
journal= {arXiv preprint arXiv:2405.11353},
year = {2024}
}
Comments
8 pages, 5 figures, and two tables. To download the source code, see https://github.com/Dragon201701/NTTSuite