Publication Details
Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity
Genetic programming, Cartesian genetic programming, cryptography, multiplicative
complexity, optimization.
The multiplicative complexity (MC) is a cryptographic criterion that describes
the vulnerability of a Boolean function to certain algebraic attacks, and in many
important cryptographic applications also determines the computational cost. In
this paper, we use Cartesian genetic programming to find various types of
cryptographic Boolean functions, improve their implementation to achieve the
minimal MC, and examine how difficult these optimized functions are to find in
comparison to functions than only need to satisfy some base cryptographic
criteria. To provide a comparison with other state-of-the-art optimization
approaches, we also use our method to improve the implementation of several
generic benchmark circuits. Our results provide new upper limits on MC of certain
functions, show that our approach is competitive, and also that finding functions
with an implementation that has better MC is not mutually exclusive with
improving other performance criteria.
@inproceedings{BUT168245,
author="Jakub {Husa} and Lukáš {Sekanina}",
title="Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity",
booktitle="2020 IEEE Congress on Evolutionary Computation (CEC)",
year="2020",
pages="1--8",
publisher="IEEE Computational Intelligence Society",
address="Los Alamitos",
doi="10.1109/CEC48606.2020.9185517",
isbn="978-1-7281-6929-3"
}