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The Complexity of Distinguishing Distributions
"... Abstract. 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 approximat ..."
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Abstract. 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. 1
A Lower Bound for the Nonlinearity of Exponential Welch Costas Functions
, 2011
"... Abstract. We study the nonlinearity of Exponential Welch Costas functions ..."
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Abstract. We study the nonlinearity of Exponential Welch Costas functions
Analysis of ARX Functions: Pseudolinear Methods for Approximation, Differentials, and Evaluating Diffusion
"... Abstract. This paper explores the approximation of addition mod 2n by addition mod 2w, where 1 ≤ w ≤ n, in ARX functions that use large words (e.g., 32bit words or 64bit words). Three main areas are explored. First, pseudolinear approximations aim to approximate the bits of a wbit window of the ..."
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Abstract. This paper explores the approximation of addition mod 2n by addition mod 2w, where 1 ≤ w ≤ n, in ARX functions that use large words (e.g., 32bit words or 64bit words). Three main areas are explored. First, pseudolinear approximations aim to approximate the bits of a wbit window of the state after some rounds. Second, the methods used in these approximations are also used to construct truncated differentials. Third, branch number metrics for diffusion are examined for ARX functions with large words, and variants of the differential and linear branch number characteristics based on pseudolinear methods are introduced. These variants are called effective differential branch number and effective linear branch number, respectively. Applications of these approximation, differential, and diffusion evaluation techniques are demonstrated on Threefish256 and Threefish512. 1