Work

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

Year: 2013

Type: article

Abstract: This brief approximates the hidden Markov model of chaotic-map truly random number generators (TRNGs) and describes its fundamental limits based on the approximate entropy rate of the underlying bit-generation process. It is demonstrated that the 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. Finally, the optimality conditions for postprocessing units that extract truly random bits from a raw random number generator are derived.

Source: IEEE Transactions on Circuits & Systems II Express Briefs

DOI: 10.1109/tcsii.2013.2258274

Authors: Ahmad Beirami, Hamid Nejati

Institutions: Georgia Institute of Technology, University of Michigan

Language: English

Topic: Chaos-based Image/Signal Encryption

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2149404985

FWCI: 3.89

Cites: 20

Cited by: 36

Related to: 10