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Differential-Linear Cryptanalysis Revisited

Authors: Gregor Leander; Kaisa Nyberg; Céline Blondeau;

Differential-Linear Cryptanalysis Revisited

Abstract

Block ciphers are arguably the most widely used type of cryptographic primitives. We are not able to assess the security of a block cipher as such, but only its security against known attacks. The two main classes of attacks are linear and differential attacks and their variants. While a fundamental link between differential and linear cryptanalysis was already given in 1994 by Chabaud and Vaudenay, these attacks have been studied independently. Only recently, in 2013, Blondeau and Nyberg used the link to compute the probability of a differential given the correlations of many linear approximations. On the cryptanalytical side, differential and linear attacks have been applied on different parts of the cipher and then combined to one distinguisher over the cipher. This method is known since 1994 when Langford and Hellman presented the first differential-linear cryptanalysis of the DES. In this paper we take the natural step and apply the theoretical link between linear and differential cryptanalysis to differential-linear cryptanalysis to develop a concise theory of this method. We give an exact expression of the bias of a differential-linear approximation in a closed form under the sole assumption that the two parts of the cipher are independent. We also show how, under a clear assumption, to approximate the bias efficiently, and perform experiments on it. In this sense, by stating minimal assumptions, we hereby complement and unify the previous approaches proposed by Biham et al. in 2002-2003, Liu et al. in 2009, and Lu in 2012, to the study of the method of differential-linear cryptanalysis.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
60
Top 1%
Top 10%
Top 10%
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