全同态加密的实用解决方案:现有加速方法调查分析

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Cybersecurity Pub Date : 2024-03-01 DOI:10.1186/s42400-023-00187-4
Yanwei Gong, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić, Jianhua Wang, Haoran Zhu
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引用次数: 0

摘要

全同态加密(FHE)在理论上取得了长足的发展和不断的突破,使其能够广泛应用于各种领域,如外包计算和安全多方计算,以保护隐私。然而,FHE 的应用受到其巨大计算开销和存储成本的限制。针对这些问题,研究人员提出了实用的加速解决方案。本文旨在提供一份全面的调查报告,系统地比较和分析 FHE 加速方案的优缺点,这是目前文献中所缺乏的。本文对 2019 年至 2022 年期间开展的相关研究进行了调查。首先,我们全面总结了有关加速 FHE 的最新研究成果,旨在为对 FHE 加速感兴趣的研究人员提供有价值的见解。其次,我们从算法和硬件角度对现有加速方案进行了分类。我们还提出了评估指标,并对各种方法进行了详细比较。最后,我们的研究提出了未来 FHE 加速的研究方向,并为该领域的实际应用和理论研究提供了指导和支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Practical solutions in fully homomorphic encryption: a survey analyzing existing acceleration methods

Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.

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来源期刊
Cybersecurity
Cybersecurity Computer Science-Information Systems
CiteScore
7.30
自引率
0.00%
发文量
77
审稿时长
9 weeks
期刊最新文献
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