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Cryptanalysis of Stream Ciphers with Linear Masking

Authors: Don Coppersmith; Shai Halevi; Charanjit S. Jutla;

Cryptanalysis of Stream Ciphers with Linear Masking

Abstract

We describe a cryptanalytical technique for distinguishing some stream ciphers from a truly random process. Roughly, the ciphers to which this method applies consist of a "non-linear process" (say, akin to a round function in block ciphers), and a "linear process" such as an LFSR (or even fixed tables). The output of the cipher can be the linear sum of both processes. To attack such ciphers, we look for any property of the "non-linear process" that can be distinguished from random. In addition, we look for a linear combination of the linear process that vanishes. We then consider the same linear combination applied to the cipher's output, and try to find traces of the distinguishing property.In this report we analyze two specific "distinguishing properties". One is a linear approximation of the non-linear process, which we demonstrate on the stream cipher SNOW. This attack needs roughly 295 words of output, with work-load of about 2100. The other is a "low-diffusion" attack, that we apply to the cipher Scream-0. The latter attack needs only about 243 bytes of output, using roughly 250 space and 280 time.

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selected citations
These citations are derived from selected sources.
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!
70
Top 10%
Top 1%
Top 10%
bronze