We present a Particle Swarm Optimizer for generating boolean functions with good cryptographic properties. The proposed algorithm updates the particles positions while preserving their Hamming weights, to ensure that the generated functions are balanced, and it adopts Hill Climbing to further improve their nonlinearity and correlation immunity. The results of the optimization experiments for n = 7 to n = 12 variables show that this new PSO algorithm finds boolean functions with good trade-offs of nonlinearity, resiliency and Strict Avalanche Criterion.
Mariot, L., Leporati, A. (2015). Heuristic search by particle swarm optimization of boolean functions for cryptographic applications. In GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp.1425-1426). Association for Computing Machinery, Inc [10.1145/2739482.2764674].
Heuristic search by particle swarm optimization of boolean functions for cryptographic applications
MARIOT, LUCAPrimo
;LEPORATI, ALBERTO OTTAVIOUltimo
2015
Abstract
We present a Particle Swarm Optimizer for generating boolean functions with good cryptographic properties. The proposed algorithm updates the particles positions while preserving their Hamming weights, to ensure that the generated functions are balanced, and it adopts Hill Climbing to further improve their nonlinearity and correlation immunity. The results of the optimization experiments for n = 7 to n = 12 variables show that this new PSO algorithm finds boolean functions with good trade-offs of nonlinearity, resiliency and Strict Avalanche Criterion.
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