Using evolutionary computation to create vectorial Boolean functions with low differential uniformity and high nonlinearity
This work addresses a specific need in cryptography for designing more secure S-boxes, but it appears incremental as it applies known metaheuristics to a known bottleneck without introducing a new paradigm.
The paper tackled the problem of creating vectorial Boolean functions for block cipher S-boxes by focusing on low differential uniformity, a criterion previously overlooked in favor of nonlinearity and autocorrelation, using evolutionary computation methods like simulated annealing, memetic algorithms, and ant colony optimization, but no concrete numerical results are reported in the abstract.
The two most important criteria for vectorial Boolean functions used as S-boxes in block ciphers are differential uniformity and nonlinearity. Previous work in this field has focused only on nonlinearity and a different criterion, autocorrelation. In this paper, we describe the results of experiments in using simulated annealing, memetic algorithms, and ant colony optimisation to create vectorial Boolean functions with low differential uniformity. Keywords: Metaheuristics, simulated annealing, memetic algorithms, ant colony optimization, cryptography, Boolean functions, vectorial Boolean functions.