Cryptography often meets the problem of distinguishing distributions. In this paper we review techniques from hypothesis testing to express the advantage of the best distinguisher limited to a given number of samples. We link it with the Chernoff information and provide a useful approximation based on the squared Euclidean distance. We use it to extend linear cryptanalysis to groups with order larger than 2. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Baignères, T., & Vaudenay, S. (2008). The complexity of distinguishing distributions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5155 LNCS, pp. 210–222). https://doi.org/10.1007/978-3-540-85093-9_20
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