Publication detail

Designing Bent Boolean Functions With Parallelized Linear Genetic Programming

HUSA, J. DOBAI, R.

Original Title

Designing Bent Boolean Functions With Parallelized Linear Genetic Programming

Type

conference paper

Language

English

Original Abstract

Bent Boolean functions are cryptographic primitives essential for the safety of cryptographic algorithms, providing a degree of non-linearity to otherwise linear systems. The maximum possible non-linearity of a Boolean function is limited by the number of its inputs, and as technology advances, functions with higher number of inputs are required in order to guarantee a level of security demanded in many modern applications. Genetic programming has been successfully used to discover new larger bent Boolean functions in the past. This paper proposes the use of linear genetic programming for this purpose. It shows that this approach is suitable for designing of bent Boolean functions larger than those designed using other approaches, and explores the influence of multiple evolutionary parameters on the evolution runtime. Parallelized implementation of the proposed approach is used to search for new, larger bent functions, and the results are compared with other related work. The results show that linear genetic programming copes better with growing number of function inputs than genetic programming, and is able to create significantly larger bent functions in comparable time.

Keywords

Bent Boolean functions, nonlinearity, parallelization, linear programming.

Authors

HUSA, J.; DOBAI, R.

Released

15. 7. 2017

Publisher

Association for Computing Machinery

Location

Berlín

ISBN

978-1-4503-4939-0

Book

GECCO Companion '17 Proceedings of the Companion Publication of the 2017 on Genetic and Evolutionary Computation Conference

Pages from

1825

Pages to

1832

Pages count

8

BibTex

@inproceedings{BUT144423,
  author="Jakub {Husa} and Roland {Dobai}",
  title="Designing Bent Boolean Functions With Parallelized Linear Genetic Programming",
  booktitle="GECCO Companion '17 Proceedings of the Companion Publication of the 2017 on Genetic and Evolutionary Computation Conference",
  year="2017",
  pages="1825--1832",
  publisher="Association for Computing Machinery",
  address="Berlín",
  doi="10.1145/3067695.3084220",
  isbn="978-1-4503-4939-0"
}