Paper 2023/1429

Leveraging GPU in Homomorphic Encryption: Framework Design and Analysis of BFV Variants

Shiyu Shen, Fudan University
Hao Yang, Nanjing University of Aeronautics and Astronautics
Wangchen Dai, Zhejiang Lab
Lu Zhou, Nanjing University of Aeronautics and Astronautics
Zhe Liu, Zhejiang Lab
Yunlei Zhao, Fudan University
Abstract

Homomorphic Encryption (HE) enhances data security by facilitating computations on encrypted data, opening new paths for privacy-focused computations. The Brakerski-Fan-Vercauteren (BFV) scheme, a promising HE scheme, raises considerable performance challenges. Graphics Processing Units (GPUs), with considerable parallel processing abilities, have emerged as an effective solution. In this work, we present an in-depth study focusing on accelerating and comparing BFV variants on GPUs, including Bajard-Eynard-Hasan-Zucca (BEHZ), Halevi-Polyakov-Shoup (HPS), and other recent variants. We introduce a universal framework accommodating all variants, propose optimized BEHZ implementation, and first support HPS variants with large parameter sets on GPUs. Moreover, we devise several optimizations for both low-level arithmetic and high-level operations, including minimizing instructions for modular operations, enhancing hardware utilization for base conversion, implementing efficient reuse strategies, and introducing intra-arithmetic and inner-conversion fusion methods, thus decreasing the overall computational and memory consumption. Leveraging our framework, we offer comprehensive comparative analyses. Our performance evaluation showcases a marked speed improvement, achieving 31.9× over OpenFHE running on a multi-threaded CPU and 39.7% and 29.9% improvement, respectively, over the state-of-the-art GPU BEHZ implementation. Our implementation of the leveled HPS variant records up to 4× speedup over other variants, positioning it as a highly promising alternative for specific applications.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint.
Keywords
Homomorphic EncryptionBFVGPU accelerationparallel processing
Contact author(s)
shenshiyu21 @ m fudan edu cn
crypto @ d4rk dev
w dai @ my cityu edu hk
lu zhou @ nuaa edu cn
zhe liu @ nuaa edu cn
ylzhao @ fudan edu cn
History
2023-09-24: approved
2023-09-21: received
See all versions
Short URL
https://ia.cr/2023/1429
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/1429,
      author = {Shiyu Shen and Hao Yang and Wangchen Dai and Lu Zhou and Zhe Liu and Yunlei Zhao},
      title = {Leveraging {GPU} in Homomorphic Encryption: Framework Design and Analysis of {BFV} Variants},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1429},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1429}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.