Welcome to the resource topic for 2023/1429
Title:
Leveraging GPU in Homomorphic Encryption: Framework Design and Analysis of BFV Variants
Authors: Shiyu Shen, Hao Yang, Wangchen Dai, Lu Zhou, Zhe Liu, Yunlei Zhao
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.
ePrint: https://eprint.iacr.org/2023/1429
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