Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138697" target="_blank" >RIV/00216305:26230/20:PU138697 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CEC48606.2020.9185517" target="_blank" >http://dx.doi.org/10.1109/CEC48606.2020.9185517</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC48606.2020.9185517" target="_blank" >10.1109/CEC48606.2020.9185517</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-10137S" target="_blank" >GA19-10137S: Navrhování a využívání knihoven aproximativních obvodů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2020 IEEE Congress on Evolutionary Computation (CEC)

ISBN

978-1-7281-6929-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE Computational Intelligence Society

Místo vydání

Los Alamitos

Místo konání akce

Glasgow

Datum konání akce

19. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000703998200029