Modifying Bent Functions to Obtain the Balanced Ones with High Nonlinearity

Maitra, Subhamoy; Mandal, Bimal; Roy, Manmatha

doi:10.1007/978-3-031-22912-1_20

Cited by 1 publication

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“…We have seen in Section 5 that there are 4-variable WPB functions reaching U 2 . However, in Section 5.1 we did not found WPB functions with nonlinearity reaching U 3 , but some attaining 116, which corresponds to the upper bound conjectured by Dobbertin [Dob95] and recently studied in [MMM22]. In 16 variables we did not observe WPB functions reaching this upper bound.…”

Section: Discussionmentioning

confidence: 44%

Weightwise Perfectly Balanced Functions and Nonlinearity

Gini

Méaux

2023

Lecture Notes in Computer Science

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In this article we realize a general study on the nonlinearity of weightwise perfectly balanced (WPB) functions. First, we derive upper and lower bounds on the nonlinearity from this class of functions for all n. Then, we give a general construction that allows us to provably provide WPB functions with nonlinearity as low as 2 n/2−1 and WPB functions with high nonlinearity, at least 2 n−1 − 2 n/2 . We provide concrete examples in 8 and 16 variables with high nonlinearity given by this construction. In 8 variables we experimentally obtain functions reaching a nonlinearity of 116 which corresponds to the upper bound of Dobbertin's conjecture, and it improves upon the maximal nonlinearity of WPB functions recently obtained with genetic algorithms. Finally, we study the distribution of nonlinearity over the set of WPB functions. We examine the exact distribution for n = 4 and provide an algorithm to estimate the distributions for n = 8 and 16, together with the results of our experimental studies for n = 8 and 16.

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Section: Discussionmentioning

confidence: 44%