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A New Method for Searching Optimal Differential and Linear Trails in ARX Ciphers

机译:一种在ARX Ciphers中搜索最佳差分和线性小径的新方法

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In this paper, we propose an automatic tool to search for optimal differential and linear trails in ARX ciphers. It’s shown that a modulo addition can be divided into sequential small modulo additions with carry bit, which turns an ARX cipher into an S-box-like cipher. From this insight, we introduce the concepts of carry-bit-dependent difference distribution table (CDDT) and carry-bit-dependent linear approximation table (CLAT). Based on them, we give efficient methods to trace all possible output differences and linear masks of a big modulo addition, with returning their differential probabilities and linear correlations simultaneously. Then an adapted Matsui’s algorithm is introduced, which can find the optimal differential and linear trails in ARX ciphers. Besides, the superiority of our tool’s potency is also confirmed by experimental results for round-reduced versions of HIGHT and SPECK. More specifically, we find the optimal differential trails for up to 10 rounds of HIGHT, reported for the first time. We also find the optimal differential trails for 10, 12, 16, 8 and 8 rounds of SPECK32/48/64/96/128, and report the provably optimal differential trails for SPECK48 and SPECK64 for the first time. The optimal linear trails for up to 9 rounds of HIGHT are reported for the first time, and the optimal linear trails for 22, 13, 15, 9 and 9 rounds of SPECK32/48/64/96/128 are also found respectively. These results evaluate the security of HIGHT and SPECK against differential and linear cryptanalysis. Also, our tool is useful to estimate the security in the design of ARX ciphers.
机译:在本文中,我们提出了一种自动工具,用于搜索ARX Cipers中的最佳差分和线性路径。结果表明,可以将模数添加到具有携带位的顺序小模数添加,这将ARX密码变为类似的S盒。从这个洞察力来看,我们介绍了携带位依赖性分布表(CDDT)和携带位相关的线性近似表(CLAT)的概念。基于它们,我们提供了高效的方法来追踪大模子添加的所有可能的输出差异和线性掩模,并同时返回其差分概率和线性相关性。然后介绍了一种适应的Matsui算法,可以在ARX Cipers中找到最佳差分和线性路径。此外,我们的工具效力的优越性也通过实验结果证实了High-Syper的Hight和Speck的实验结果。更具体地说,我们首次报道了最多10轮高达10轮高度的最佳差分径。我们还发现了10,12,16,8和8轮Speck32 / 48/64 / 96/128的最佳差分路径,并首次报告Speck48和Speck64的可透明的最佳差分路径。首次报告最多9轮高位的最佳线性跟踪,并且还分别发现了22,13,15,9和9轮的最佳线性路径也发现了Speck32 / 48/64 / 96/128。这些结果评估了对抗差分和线性密码分析的Hight和Speck的安全性。此外,我们的工具很有用来估计ARX CIPHERS设计中的安全性。

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