Luca Mariot

Heuristic Search by Particle Swarm Optimization of Boolean Functions for Cryptographic Applications

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.

A Genetic Algorithm for Evolving Plateaued Cryptographic Boolean Functions

We propose a genetic algorithm GA to search for plateaued boolean functions, which represent suitable candidates for the design of stream ciphers due to their good cryptographic properties. Using the spectral inversion technique introduced by Clark, Jacob, Maitra and Stanica, our GA encodes the chromosome of a candidate solution as a permutation of a three-valued Walsh spectrum. Additionally, we design specialized crossover and mutation operators so that the swapped positions in the offspring chromosomes correspond to different values in the resulting Walsh spectra. Some tests performed on the set of pseudoboolean functions of

Sharing Secrets by Computing Preimages of Bipermutive Cellular Automata

n=6

Evolutionary algorithms for the design of orthogonal latin squares based on cellular automata

and

Design of S-boxes Defined with Cellular Automata Rules

n=7

Asynchrony Immune Cellular Automata

variables show that in the former case our GA outperforms Clark et al.s simulated annealing algorithm with respect to the ratio of generated plateaued boolean functions per number of optimization runs.