Distinguishing attack on SOBER-128 with linear masking

Authors:
Joo Yeon Cho;Josef Pieprzyk
Affiliations:
Centre for Advanced Computing – Algorithms and Cryptography, Department of Computing, Macquarie University, NSW, Australia;Centre for Advanced Computing – Algorithms and Cryptography, Department of Computing, Macquarie University, NSW, Australia
Venue:
ACISP'06 Proceedings of the 11th Australasian conference on Information Security and Privacy
Year:
2006

Citing 2
Cited 3

Distinguishing Attacks on SOBER-t16 and t32

FSE '02 Revised Papers from the 9th International Workshop on Fast Software Encryption
Cryptanalysis of Stream Ciphers with Linear Masking

CRYPTO '02 Proceedings of the 22nd Annual International Cryptology Conference on Advances in Cryptology

A practical distinguisher for the Shannon cipher

Journal of Systems and Software
Improved linear cryptanalysis of SOSEMANUK

ICISC'09 Proceedings of the 12th international conference on Information security and cryptology
Determining quality of s-boxes using pseudo random sequences generated from stream ciphers

ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a distinguishing attack against SOBER-128 with linear masking. We found a linear approximation which has a bias of 2−−8.8 for the non-linear filter. The attack applies the observation made by Ekdahl and Johansson that there is a sequence of clocks for which the linear combination of some states vanishes. This linear dependency allows that the linear masking method can be applied. We also show that the bias of the distinguisher can be improved (or estimated more precisely) by considering quadratic terms of the approximation. The probability bias of the quadratic approximation used in the distinguisher is estimated to be equal to O(2−−51.8), so that we claim that SOBER-128 is distinguishable from truly random cipher by observing O(2103.6) keystream words.