SoK: Modeling for Large S-boxes Oriented to Differential Probabilities and Linear Correlations (Long Paper)

IF 1.7 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Ling Sun, Meiqin Wang
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引用次数: 1

Abstract

Automatic methods for differential and linear characteristic search are well-established at the moment. Typically, the designers of novel ciphers also give preliminary analytical findings for analysing the differential and linear properties using automatic techniques. However, neither MILP-based nor SAT/SMT-based approaches have fully resolved the problem of searching for actual differential and linear characteristics of ciphers with large S-boxes. To tackle the issue, we present three strategies for developing SAT models for 8-bit S-boxes that are geared toward differential probabilities and linear correlations. While these approaches cannot guarantee a minimum model size, the time needed to obtain models is drastically reduced. The newly proposed SAT model for large S-boxes enables us to establish that the upper bound on the differential probability for 14 rounds of SKINNY-128 is 2−131, thereby completing the unsuccessful work of Abdelkhalek et al. We also analyse the seven AES-based constructions C1 - C7 designed by Jean and Nikolić and compute the minimum number of active S-boxes necessary to cause an internal collision using the SAT method. For two constructions C3 and C5, the current lower bound on the number of active S-boxes is increased, resulting in a more precise security analysis for these two structures.
SoK:面向微分概率和线性相关性的大s盒建模(长论文)
微分和线性特征搜索的自动方法目前已经建立。通常,新密码的设计者也给出了初步的分析结果,用于分析微分和线性性质使用自动技术。然而,无论是基于milp的方法还是基于SAT/ smt的方法都不能完全解决具有大s盒的密码的实际微分和线性特征的搜索问题。为了解决这个问题,我们提出了三种策略来开发面向微分概率和线性相关性的8位s盒的SAT模型。虽然这些方法不能保证最小的模型大小,但获得模型所需的时间大大减少了。新提出的大s盒SAT模型使我们能够确定14轮SKINNY-128的微分概率上界为2−131,从而完成了Abdelkhalek等人的失败工作。我们还分析了Jean和nikoliic设计的七种基于aes的结构C1 - C7,并使用SAT方法计算了引起内部碰撞所需的最小主动s盒数。对于两种结构C3和C5,有效s盒数量的当前下界增加,从而使这两种结构的安全性分析更加精确。
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
IACR Transactions on Symmetric Cryptology
IACR Transactions on Symmetric Cryptology Mathematics-Applied Mathematics
CiteScore
5.50
自引率
22.90%
发文量
37
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