Abstract
In this article we study the Algebraic Immunity (AI) of Weightwise Perfectly Balanced (WPB) functions. After showing a lower bound on the AI of two classes of WPB functions from the previous literature, we prove that the minimal AI of a WPB n-variables function is constant, equal to 2 for \(n\ge 4\). Then, we compute the distribution of the AI of WPB function in 4 variables, and estimate the one in 8 and 16 variables. For these values of n we observe that a large majority of WPB functions have optimal AI, and that we could not obtain a WPB function with AI 2 by sampling at random. Finally, we address the problem of constructing WPB functions with bounded algebraic immunity, exploiting a construction from [12]. In particular, we present a method to generate multiple WPB functions with minimal AI, and we prove that the WPB functions with high nonlinearity exhibited in [12] also have minimal AI. We conclude with a construction giving WPB functions with lower bounded AI, and give as example a family with all elements with AI at least \(n/2-\log (n)+1\).
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Gini, A., Méaux, P. (2023). On the Algebraic Immunity of Weightwise Perfectly Balanced Functions. In: Aly, A., Tibouchi, M. (eds) Progress in Cryptology – LATINCRYPT 2023. LATINCRYPT 2023. Lecture Notes in Computer Science, vol 14168. Springer, Cham. https://doi.org/10.1007/978-3-031-44469-2_1
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