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On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension
On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension
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On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension
On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension

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On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension
On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension
Journal Article

On Second-Order Derivatives of Boolean Functions and Cubic APN Permutations in Even Dimension

2024
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Overview
The big APN problem is one of the most important challenges in the theory of Boolean functions, i.e. finding a new APN permutation in even dimension. Among this class of functions, those with the lowest possible degree are cubic. Yet, none has been found so far. In this paper, we introduce new parameters for Boolean functions and for vectorial Boolean functions, mostly derived from the behavior of their second-order derivatives. These parameters are invariant under extended affine equivalence, and they are particularly relevant for small-degree functions. They allow studying bent, semi-bent and APN functions of degrees two and three. In particular, they allow tackling the big APN problem for cubic permutations. Notably, we focus on the case of dimension 8, providing some computational results.
Publisher
Springer International Publishing,Springer Nature B.V