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https://doi.org/10.1007/3-540-...
Part of book or chapter of book . 2002 . Peer-reviewed
License: Springer TDM
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Enhancing Differential-Linear Cryptanalysis

Authors: Nathan Keller; Orr Dunkelman; Eli Biham;

Enhancing Differential-Linear Cryptanalysis

Abstract

Differential cryptanalysis analyzes ciphers by studying the development of differences during encryption. Linear cryptanalysis is similar but is based on studying approximate linear relations. In 1994, Langford and Hellman showed that both kinds of analysis can be combined together by a technique called differential-linear cryptanalysis, in which the differential part creates a linear approximation with probability 1. They applied their technique to 8-round DES. In this paper we present an enhancement of differential-linear cryptanalysis in which the inherited linear probability is smaller than 1. We use this extension to describe a differential-linear distinguisher for a 7-round reducedversion of DES, and to present the best known key-recovery attack on a 9-round reduced-version of DES. We use our enhanced technique to attack COCONUT98 with time complexity 233.7 encryptions and 227.7 chosen plaintexts.

  • BIP!
    Impact byBIP!
    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).
    72
    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.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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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!
72
Top 10%
Top 10%
Top 10%
bronze