Publication Details

Designing Correlation Immune Boolean Functions With Minimal Hamming Weight Using Various Genetic Programming Methods

HUSA Jakub. Designing Correlation Immune Boolean Functions With Minimal Hamming Weight Using Various Genetic Programming Methods. In: GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. Praha: Association for Computing Machinery, 2019, pp. 342-343. ISBN 978-1-4503-6748-6.
Czech title
Návrh korelaci imunních booleovských funkcí s minimální Hammingovou vahou pomocí různých metod genetického programování
Type
conference paper
Language
english
Authors
Husa Jakub, Ing., Ph.D. (DCSY FIT BUT)
Keywords


Evolutionary algorithms, Genetic Programming, Cartesian Genetic
Programming, Linear Genetic Programming, Cryptography, Boo-
lean Functions, Hamming Weight, Correlation Immunity.

Abstract

In this paper we search for Boolean functions with properties that make them useful in cryptography for preventing side-channel attacks. To find these functions we use three genetic programming methods, including linear genetic programming, which has not been used to design these functions before. Our results aim to provide a fair comparison between the methods, by searching for each method's best performing parameters, and provide an insight into how well they cope with growing number of function inputs and demand for higher correlation immunity.

Published
2019
Pages
342-343
Proceedings
GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion
Conference
Genetic and Evolutionary Computations Conference 2019, Praha, CZ
ISBN
978-1-4503-6748-6
Publisher
Association for Computing Machinery
Place
Praha, CZ
DOI
10.1145/3319619.3321925
UT WoS
000538328100171
EID Scopus
2-s2.0-85070607161
BibTeX 
@INPROCEEDINGS{FITPUB11919,
   author = "Jakub Husa",
   title = "Designing Correlation Immune Boolean Functions With Minimal Hamming Weight Using Various Genetic Programming Methods",
   pages = "342--343",
   booktitle = "GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion",
   year = 2019,
   location = "Praha, CZ",
   publisher = "Association for Computing Machinery",
   ISBN = "978-1-4503-6748-6",
   doi = "10.1145/3319619.3321925",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11919"
}
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