Novel Implementation of High-Performance Polynomial Multiplication for Unified KEM Saber based on TMVP Design Strategy

Pengzhou He, Jiafeng Xie
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Abstract

The rapid advancement in quantum technology has initiated a new round of exploration of efficient implementation of post-quantum cryptography (PQC) on hardware platforms. Key encapsulation mechanism (KEM) Saber, a module lattice-based PQC, is one of the four encryption scheme finalists in the third-round National Institute of Standards and Technology (NIST) standardization process. In this paper, we propose a novel Toeplitz Matrix-Vector Product (TMVP)-based design strategy to efficiently implement polynomial multiplication (essential arithmetic operation) for KEM Saber. The proposed work consists of three layers of interdependent efforts: (i) first of all, we have formulated the polynomial multiplication of KEM Saber into a desired mathematical form for further developing into the proposed TMVP-based algorithm for high-performance operation; (ii) then, we have followed the proposed TMVP-based algorithm to innovatively transfer the derived algorithm into a unified polynomial multiplication structure (fits all security ranks) with the help of a series of algorithm-to-architecture co-implementation/mapping techniques; (iii) finally, detailed implementation results and complexity analysis have confirmed the efficiency of the proposed TMVP design strategy. Specifically, the field-programmable gate array (FPGA) implementation results show that the proposed design has at least less 30.92% area-delay product (ADP) than the competing ones.
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基于TMVP设计策略的统一KEM军刀高性能多项式乘法新实现
量子技术的快速发展引发了在硬件平台上高效实现后量子密码术(PQC)的新一轮探索。密钥封装机制(KEM) Saber是一种基于模块格的PQC,是美国国家标准与技术研究院(NIST)第三轮标准化过程中的四个加密方案决赛选手之一。本文提出了一种新的基于Toeplitz矩阵向量积(TMVP)的设计策略,以有效地实现KEM Saber的多项式乘法(基本算术运算)。提出的工作包括三层相互依存的努力:(i)首先,我们将KEM Saber的多项式乘法公式化为所需的数学形式,以便进一步发展为提出的基于tmvp的高性能操作算法;(ii)然后,我们根据提出的基于tmvp的算法,借助一系列算法到架构的协同实现/映射技术,创新地将导出的算法转换为统一的多项式乘法结构(适用于所有安全级别);(iii)最后,详细的实施结果和复杂性分析证实了所提出的TMVP设计策略的有效性。具体而言,现场可编程门阵列(FPGA)的实现结果表明,该设计比竞争对手的面积延迟积(ADP)至少低30.92%。
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