Paper 2016/178

Integrals go Statistical: Cryptanalysis of Full Skipjack Variants

Meiqin Wang, Tingting Cui, Huaifeng Chen, Ling Sun, Long Wen, and Andrey Bogdanov

Abstract

Integral attacks form a powerful class of cryptanalytic techniques that have been widely used in the security analysis of block ciphers. The integral distinguishers are based on balanced properties holding with probability one. To obtain a distinguisher covering more rounds, an attacker will normally increase the data complexity by iterating through more plaintexts with a given structure under the strict limitation of the full codebook. On the other hand, an integral property can only be deterministically verified if the plaintexts cover all possible values of a bit selection. These circumstances have somehow restrained the applications of integral cryptanalysis. In this paper, we aim to address these limitations and propose a novel \emph{statistical integral distinguisher} where only a part of value sets for these input bit selections are taken into consideration instead of all possible values. This enables us to achieve significantly lower data complexities for our statistical integral distinguisher as compared to those of traditional integral distinguisher. As an illustration, we successfully attack the full-round Skipjack-BABABABA for the first time, which is the variant of NSA's Skipjack block cipher.