Class of Trustworthy Pseudo-Random Number Generators

TL;DR

This paper introduces a new class of trustworthy pseudo-random number generators based on chaos theory and graph structures, enhancing security for cryptographic applications.

Contribution

It proposes a novel method using graph strongly connected components to select chaotic iterate functions for PRNGs, improving their robustness.

Findings

PRNGs pass the NIST statistical tests

Enhanced security features demonstrated

Novel graph-based approach for chaotic functions

Abstract

With the widespread use of communication technologies, cryptosystems are therefore critical to guarantee security over open networks as the Internet. Pseudo-random number generators (PRNGs) are fundamental in cryptosystems and information hiding schemes. One of the existing chaos-based PRNGs is using chaotic iterations schemes. In prior literature, the iterate function is just the vectorial boolean negation. In this paper, we propose a method using Graph with strongly connected components as a selection criterion for chaotic iterate function. In order to face the challenge of using the proposed chaotic iterate functions in PRNG, these PRNGs are subjected to a statistical battery of tests, which is the well-known NIST in the area of cryptography.