Abstract

Abstract Chaotic Pseudo Random Number Generators have been seen as a promising candidate for secure random number generation. Using the logistic map as state transition function, we perform number generation experiments that illustrate the challenges when trying to do a replication study. Those challenges range from uncertainties about the rounding mode in arithmetic hardware over chosen number representations for variables to compiler or programmer decisions on evaluation order for arithmetic expressions. We find that different decisions lead to different streams with different security properties, where we focus on period length. However, descriptions in articles often are not detailed enough to deduce all decisions unambiguously. To address similar problems in other replication studies for security applications, we propose recommendations for descriptions of numerical experiments on security applications to avoid the above challenges. Moreover, we use the results to propose the use of higher-radix and mixed-radix representations to trade storage size for period length, and investigate if exploiting the symmetry of the logistic map function for number representation is advantageous.

Highlights

  • Chaotic pseudo-random number generators, in particular the logistic map [1], have been considered as a means for secure PRNGs

  • We find that different decisions lead to different streams with different security properties, where we focus on period length

  • We use the results to propose the use of higher-radix and mixed-radix representations to trade storage size for period length, and investigate if exploiting the symmetry of the logistic map function for number representation is advantageous

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Summary

Introduction

Chaotic pseudo-random number generators (cPRNGs), in particular the logistic map [1], have been considered as a means for secure PRNGs. When studying the descriptions of these experiments, we found that they are missing some implementation details that influence the period length, even for fixed parameter settings Replicating those studies experiences some challenges, which we report and from which we derive recommendations for describing experiments including numeric calculations. Replication study challenges, number formats for cPRNGs logistic map function can be exploited in number representations, while Section 6 concludes with recommendations on configuration details to be reported about numerical experiments, and an outlook on future work

Chaotic pseudo-random number generation
Finite-length representations for real numbers
Related work
Challenges
Order of computations
Rounding modes
Number representation
Experiments
Comparing different number formats
Extending number formats for symmetry
Findings
Conclusions
Full Text
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