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Time-Adaptive Statistical Test for Random Number Generators.

Boris Ryabko1,2

  • 1Institute of Computational Technologies of the Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia.

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|December 8, 2020
PubMed
Summary
This summary is machine-generated.

We propose a time-adaptive battery of tests to improve random number generator (RNG) testing efficiency. This method preserves statistical test power while reducing computation time for better RNG evaluation.

Keywords:
hypothesis testingp-valuerandom number generatorsrandomness testingtest battery

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Area of Science:

  • Cryptography
  • Information Theory
  • Statistical Computing

Background:

  • Random number generators (RNGs) are crucial for security and simulations.
  • Existing statistical test batteries face a trade-off between test coverage and sequence length.
  • Larger test batteries reduce individual test time, limiting detection of subtle non-randomness.

Purpose of the Study:

  • To introduce an adaptive method for using test batteries to evaluate random number generators.
  • To mitigate the trade-off between computational time and the power of statistical tests.
  • To preserve the effectiveness of RNG testing while optimizing resource allocation.

Main Methods:

  • Development of a 'time-adaptive battery of tests' approach.
  • Leveraging a theorem on the asymptotic properties of p-values.
  • Utilizing the relationship between p-values, sequence length, and Shannon entropy for stationary ergodic sources.

Main Results:

  • The proposed method offers a way to reduce testing time without significant loss of statistical power.
  • The time-adaptive approach aims to maintain the ability to detect deviations from randomness.
  • Theoretical underpinnings are based on the convergence of normalized log p-values to a constant related to source entropy.

Conclusions:

  • The time-adaptive battery of tests presents a more efficient strategy for RNG validation.
  • This method addresses the practical limitations of current large-scale statistical test suites.
  • Further research can explore the practical implementation and empirical validation of this adaptive testing framework.