A Framework for Investigating the Performance of Chaotic-Map Truly Random Number Generators

TL;DR

This paper models chaotic-map TRNGs using hidden Markov models, highlighting the importance of entropy-rate in their performance and proposing optimal post-processing conditions for extracting true randomness.

Contribution

It introduces an approximation method for the hidden Markov model of chaotic-map TRNGs and establishes entropy-rate as a key factor in their design and robustness.

Findings

Entropy-rate influences TRNG performance and robustness.

Derived optimal conditions for post-processing units.

Provided a framework for analyzing chaotic-map TRNGs.

Abstract

In this paper, we approximate the hidden Markov model of chaotic-map truly random number generators (TRNGs) and describe its fundamental limits based on the approximate entropy-rate of the underlying bit-generation process. We demonstrate that entropy-rate plays a key role in the performance and robustness of chaotic-map TRNGs, which must be taken into account in the circuit design optimization. We further derive optimality conditions for post-processing units that extract truly random bits from a raw-RNG.