Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity
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- @InProceedings{Husa:2020:CEC,
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author = "Jakub Husa and Lukas Sekanina",
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booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Evolving Cryptographic Boolean Functions with Minimal
Multiplicative Complexity",
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year = "2020",
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editor = "Yaochu Jin",
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month = "19-24 " # jul,
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185517",
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abstract = "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.",
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notes = "Also known as \cite{9185517}",
- }
Genetic Programming entries for
Jakub Husa
Lukas Sekanina
Citations