Statistical complexity measure of pseudorandom bit generators

Pseudorandom number generators (PRNG) are extensively used in Monte Carlo simulations, gambling machines and cryptography as substitutes of ideal random number generators (RNG). Each application imposes different statistical requirements to PRNGs. As L'Ecuyer clearly states "the main goal...

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Detalles Bibliográficos
Publicado: 2005
Materias:
Random number generators
Statistical complexity
Acoustic noise
Algorithms
Computational complexity
Computer simulation
Monte Carlo methods
Public key cryptography
Statistical mechanics
Chaotic dynamical systems
Noise sources
Random processes
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v354_n1-4_p281_Gonzalez
http://hdl.handle.net/20.500.12110/paper_03784371_v354_n1-4_p281_Gonzalez
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03784371_v354_n1-4_p281_Gonzalez
record_format dspace
spelling paper:paper_03784371_v354_n1-4_p281_Gonzalez2023-06-08T15:40:02Z Statistical complexity measure of pseudorandom bit generators Random number generators Statistical complexity Acoustic noise Algorithms Computational complexity Computer simulation Monte Carlo methods Public key cryptography Statistical mechanics Chaotic dynamical systems Noise sources Random number generators Statistical complexity Random processes Pseudorandom number generators (PRNG) are extensively used in Monte Carlo simulations, gambling machines and cryptography as substitutes of ideal random number generators (RNG). Each application imposes different statistical requirements to PRNGs. As L'Ecuyer clearly states "the main goal for Monte Carlo methods is to reproduce the statistical properties on which these methods are based whereas for gambling machines and cryptology, observing the sequence of output values for some time should provide no practical advantage for predicting the forthcoming numbers better than by just guessing at random". In accordance with different applications several statistical test suites have been developed to analyze the sequences generated by PRNGs. In a recent paper a new statistical complexity measure [Phys. Lett. A 311 (2003) 126] has been defined. Here we propose this measure, as a randomness quantifier of a PRNGs. The test is applied to three very well known and widely tested PRNGs available in the literature. All of them are based on mathematical algorithms. Another PRNGs based on Lorenz 3D chaotic dynamical system is also analyzed. PRNGs based on chaos may be considered as a model for physical noise sources and important new results are recently reported. All the design steps of this PRNG are described, and each stage increase the PRNG randomness using different strategies. It is shown that the MPR statistical complexity measure is capable to quantify this randomness improvement. The PRNG based on the chaotic 3D Lorenz dynamical system is also evaluated using traditional digital signal processing tools for comparison. © 2005 Elsevier B.V. All rights reserved. 2005 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v354_n1-4_p281_Gonzalez http://hdl.handle.net/20.500.12110/paper_03784371_v354_n1-4_p281_Gonzalez
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Random number generators
Statistical complexity
Acoustic noise
Algorithms
Computational complexity
Computer simulation
Monte Carlo methods
Public key cryptography
Statistical mechanics
Chaotic dynamical systems
Noise sources
Random number generators
Statistical complexity
Random processes
spellingShingle Random number generators
Statistical complexity
Acoustic noise
Algorithms
Computational complexity
Computer simulation
Monte Carlo methods
Public key cryptography
Statistical mechanics
Chaotic dynamical systems
Noise sources
Random number generators
Statistical complexity
Random processes
Statistical complexity measure of pseudorandom bit generators
topic_facet Random number generators
Statistical complexity
Acoustic noise
Algorithms
Computational complexity
Computer simulation
Monte Carlo methods
Public key cryptography
Statistical mechanics
Chaotic dynamical systems
Noise sources
Random number generators
Statistical complexity
Random processes
description Pseudorandom number generators (PRNG) are extensively used in Monte Carlo simulations, gambling machines and cryptography as substitutes of ideal random number generators (RNG). Each application imposes different statistical requirements to PRNGs. As L'Ecuyer clearly states "the main goal for Monte Carlo methods is to reproduce the statistical properties on which these methods are based whereas for gambling machines and cryptology, observing the sequence of output values for some time should provide no practical advantage for predicting the forthcoming numbers better than by just guessing at random". In accordance with different applications several statistical test suites have been developed to analyze the sequences generated by PRNGs. In a recent paper a new statistical complexity measure [Phys. Lett. A 311 (2003) 126] has been defined. Here we propose this measure, as a randomness quantifier of a PRNGs. The test is applied to three very well known and widely tested PRNGs available in the literature. All of them are based on mathematical algorithms. Another PRNGs based on Lorenz 3D chaotic dynamical system is also analyzed. PRNGs based on chaos may be considered as a model for physical noise sources and important new results are recently reported. All the design steps of this PRNG are described, and each stage increase the PRNG randomness using different strategies. It is shown that the MPR statistical complexity measure is capable to quantify this randomness improvement. The PRNG based on the chaotic 3D Lorenz dynamical system is also evaluated using traditional digital signal processing tools for comparison. © 2005 Elsevier B.V. All rights reserved.
title Statistical complexity measure of pseudorandom bit generators
title_short Statistical complexity measure of pseudorandom bit generators
title_full Statistical complexity measure of pseudorandom bit generators
title_fullStr Statistical complexity measure of pseudorandom bit generators
title_full_unstemmed Statistical complexity measure of pseudorandom bit generators
title_sort statistical complexity measure of pseudorandom bit generators
publishDate 2005
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v354_n1-4_p281_Gonzalez
http://hdl.handle.net/20.500.12110/paper_03784371_v354_n1-4_p281_Gonzalez
_version_ 1768544368892837888

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