Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GA19-10137S" target="_blank" >GA19-10137S: Designing and exploiting libraries of approximate circuits</a><br>

Continuities

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

Publication year

2020

Confidentiality

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

Article name in the collection

2020 IEEE Congress on Evolutionary Computation (CEC)

ISBN

978-1-7281-6929-3

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

1-8

Publisher name

IEEE Computational Intelligence Society

Place of publication

Los Alamitos

Event location

Glasgow

Event date

Jul 19, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000703998200029