Faster Bootstrapping via Modulus Raising and Composite NTT

Zhihao Li, Ying Liu, Xianhui Lu, Ruida Wang, Benqiang Wei, Chunling Chen, Kunpeng Wang
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Abstract

FHEW-like schemes utilize exact gadget decomposition to reduce error growth and ensure that the bootstrapping incurs only polynomial error growth. However, the exact gadget decomposition method requires higher computation complexity and larger memory storage. In this paper, we improve the efficiency of the FHEWlike schemes by utilizing the composite NTT that performs the Number Theoretic Transform (NTT) with a composite modulus. Specifically, based on the composite NTT, we integrate modulus raising and gadget decomposition in the external product, which reduces the number of NTTs required in the blind rotation from 2(dg + 1)n to 2(⌈dg⌉/2 + 1)n. Furthermore, we develop a modulus packing technique that uses the Chinese Remainder Theorem (CRT) and composite NTT to bootstrap multiple LWE ciphertexts through one blind rotation process.We implement the bootstrapping algorithms and evaluate the performance on various benchmark computations using both binary and ternary secret keys. Our results of the single bootstrapping process indicate that the proposed approach achieves speedups of up to 1.7 x, and reduces the size of the blind rotation key by 50% under specific parameters. Finally, we instantiate two ciphertexts in the packing procedure, and the experimental results show that our technique is around 1.5 x faster than the two bootstrapping processes under the 127-bit security level.
通过模量提升和复合 NTT 加快引导速度
类fhew方案利用精确小工具分解来减少误差增长,并确保自启动只引起多项式误差增长。然而,精确的小部件分解方法需要更高的计算复杂度和更大的内存存储。在本文中,我们利用复合NTT以复合模数进行数论变换(NTT),从而提高了类fhew方案的效率。具体而言,基于复合NTT,我们在外部积中集成了模提升和小枝分解,将盲旋转所需的NTT数量从2(dg + 1)n减少到2(∑dg²/2 + 1)n。此外,我们开发了一种模填充技术,该技术使用中国剩余定理(CRT)和复合NTT通过一个盲旋转过程来引导多个LWE密文。我们实现了自举算法,并使用二进制和三元密钥在各种基准计算中评估了性能。我们的单次启动过程的结果表明,在特定参数下,所提出的方法实现了高达1.7倍的加速,并将盲旋转键的大小减少了50%。最后,我们在封装过程中实例化了两个密文,实验结果表明,在127位安全级别下,我们的技术比两个引导过程快1.5倍左右。
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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