[期刊论文]


Truncated Differential Attacks On Symmetric Primitives With Linear Key Schedule: WARP And Orthros

作   者:
Shiqi Hou;Baofeng Wu;Shichang Wang;Hao Guo;Dongdai Lin;

出版年:暂无

页    码:暂无
出版社:Oxford University Press (OUP)


摘   要:

In truncated differential cryptanalysis of symmetric primitives, a generalized framework is to search a distinguisher concerning part of output differences, like truncated differential distribution (TDD) on certain bits (e.g. a nibble) first, and then append several rounds before and after it to recover the secret key. The logarithmic likelihood ratio statistic with respect to the TDD is usually used to distinguish guessed key bits. In this paper, we study how to improve the effect of truncated differential cryptanalysis by considering key schedules of the attacked ciphers. It turns out that for a cipher with a simple key schedule, certain guessed subkey bits may reveal information of the master key, which will help build a stronger TDD distinguisher and reduce the key recovery complexity or attack more rounds. As a result, we explore heuristic techniques to search key-recovery-friendly TDDs and construct automatic search models based on MILP. The refined methods are applied to two recent designs of symmetric primitives, WARP and Orthros, together with peculiarities of their structures as well. For WARP, after making two observations on relations between certain differences with key bits, we propose an algorithm that can find TDDs with low complexities and having potentialities to cover more rounds. Consequently, we launch key recovery attacks on 24 to 27 rounds of WARP. When it comes to Orthros, we present a two-step search algorithm to balance the number of guessed key bits and TDDs, obtaining a key recovery attack on a 7-round variant of it in the weak-key setting. Finally, we perform several verification experiments on round-reduced versions of WARP and Orthros, and the experimental results are consistent with the theoretical distributions and the analysis of generalized key recovery attack framework.



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所属期刊
The Computer Journal
ISSN: 0010-4620
来自:Oxford University Press (OUP)