Vectorial Fast Correlation Attacks

Abstract

A new, vectorial approach to fast correlation attacks on binary memoryless combiners is proposed. Instead of individual input sequences or their linear combinations, the new attack is targeting subsets of input sequences as a whole, thus exploiting the full correlation between the chosen subset and the output sequence. In particular, all the input sequences can be targeted simultaneously. The attack is based on a novel iterative probabilistic algorithm which is also applicable to general memoryless combiners over finite fields or finite rings. Experimental results obtained for randomly chosen binary combiners with balanced combining functions show that the vectorial approach yields a considerable improvement in comparison with the classical, scalar approach.

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