Acceleration of the Bootstrapping in TFHE by FPGA

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jian Zhang;Aijiao Cui;Yier Jin
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Abstract

Privacy-preserving computing is playing an ever-increasingly important role in various fields. A leading example of privacy-preserving computing is Fully Homomorphic Encryption (FHE). FHE enables arbitrary computations directly on the ciphertext. This guarantees that the original data will not be disclosed while processing the data. However, FHE brings in the high computation cost which, in turn, limits the application of FHE. Among all steps of FHE, bootstrapping is a critical operation yet a bottleneck for the FHE efficiency. Torus FHE (TFHE) was presented as a method which can compute arbitrary Boolean functions on ciphertext with fast gate bootstrapping. In this paper, we show an implementation of TFHE gate bootstrapping on ZYNQ ZCU102 FPGA board. The memory operation is specially organized to facilitate the implementation of the adopted Number Theoretic Transform (NTT) of external product. Each function involved in the TFHE gate bootstrapping is implemented at the register-transfer level (RTL), and each operation is carefully scheduled to maximize the parallelism. Experimental results show that with ZCU102 working at the frequency of 300MHz, the proposed scheme can bootstrap one bit within 1.9ms on average. Compared with the accelerated TFHE using the mainstream CPU, the proposed scheme shows a 5.0X speedup. If under the similar clock frequency, it presents 1.23X faster than cuFHE which is accelerated by GPU. The proposed scheme also shows other advantages such as high efficiency and better tradeoff than existing FPGA-based acceleration schemes.
利用 FPGA 加速 TFHE 的引导过程
隐私保护计算在各个领域发挥着越来越重要的作用。隐私保护计算的一个主要例子是完全同态加密(FHE)。FHE可以直接对密文进行任意计算。这保证了在处理数据时不会泄露原始数据。然而,FHE带来了高昂的计算成本,这反过来又限制了FHE的应用。在FHE的所有步骤中,自举是一个关键的操作,但也是影响FHE效率的瓶颈。环面FHE (TFHE)是一种利用快速门自启动技术计算密文上任意布尔函数的方法。在本文中,我们展示了一种在ZYNQ ZCU102 FPGA板上实现TFHE门启动的方法。存储器操作是为了便于外部积的采用的数论变换(NTT)的实现而专门组织的。TFHE门引导中涉及的每个函数都是在寄存器传输级(RTL)实现的,并且每个操作都经过精心安排以最大化并行性。实验结果表明,在ZCU102工作频率为300MHz的情况下,该方案能在1.9ms内平均引导1位。与使用主流CPU的加速TFHE相比,该方案的速度提高了5.0倍。在相似的时钟频率下,它比GPU加速的cuFHE快1.23倍。与现有的基于fpga的加速方案相比,该方案具有效率高、折衷性好等优点。
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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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
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