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Statistical Testing of Random Number Generators and Their Improvement Using Randomness Extraction.

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Summary
This summary is machine-generated.

This study enhances random number generator (RNG) output quality using randomness extractors and post-processing methods. Statistical testing validates improvements for cryptographic applications.

Keywords:
information-theoretic securityrandom number generationrandomness extractorsstatistical testing

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Area of Science:

  • Computer Science
  • Information Security
  • Applied Mathematics

Background:

  • Building and testing random number generators (RNGs) is complex, particularly for cryptography.
  • Statistical tests are crucial for verifying RNG output quality, despite limitations in guaranteeing perfection.

Purpose of the Study:

  • To design, implement, and evaluate post-processing methods using randomness extractors to enhance RNG output.
  • To compare the performance of different RNGs and post-processing techniques through rigorous statistical testing.

Main Methods:

  • Intensive statistical testing of three RNGs: 32-bit linear feedback shift register (LFSR), Intel's RDSEED, and IDQuantique's Quantis.
  • Application of various post-processing methods (randomness extractors) to improve RNG output quality.
  • Development of a comprehensive, parameterizable statistical testing environment for evaluating RNG performance.

Main Results:

  • Comparative analysis of the baseline performance of LFSR, RDSEED, and Quantis RNGs.
  • Evaluation of the effectiveness of different post-processing techniques in improving RNG output.
  • Demonstration of the utility of the developed statistical testing environment for RNG verification.

Conclusions:

  • Post-processing methods, particularly randomness extractors, can significantly improve RNG output quality for cryptographic use.
  • Rigorous statistical testing is essential for validating the performance and security of random number generators.
  • The developed testing framework provides a flexible and comprehensive approach to RNG evaluation.