Statistical testing of random number generators and their improvement using randomness extraction

Cameron Foreman; Richie Yeung; Florian J. Curchod

Paper 2024/492

Statistical testing of random number generators and their improvement using randomness extraction

Cameron Foreman

, University College London, Quantinuum

Richie Yeung, University of Oxford, Quantinuum

Florian J. Curchod

Abstract

Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. While statistical tests cannot definitively guarantee an RNG’s output quality, they are a powerful verification tool and the only universally applicable testing method. In this work, we design, implement, and present various post-processing methods, using randomness extractors, to improve the RNG output quality and compare them through statistical testing. We begin by performing intensive tests on three RNGs—the 32-bit linear feedback shift register (LFSR), Intel’s ‘RDSEED,’ and IDQuantique’s ‘Quantis’—and compare their performance. Next, we apply the different post-processing methods to each RNG and conduct further intensive testing on the processed output. To facilitate this, we introduce a comprehensive statistical testing environment, based on existing test suites, that can be parametrised for lightweight (fast) to intensive testing.

Note: As published in the journal Entropy, 21 + 12 pages.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Published elsewhere. Entropy
DOI: 10.3390/e26121053
Keywords: statistical testing random number generator randomness extractors information-theoretic security
Contact author(s): cameron foreman @ quantinuum com
richie yeung @ quantinuum com
florian curchod @ quantinuum com
History: 2025-01-09: revised; 2024-03-27: received; See all versions
Short URL: https://ia.cr/2024/492
License: CC BY-NC

BibTeX

@misc{cryptoeprint:2024/492,
      author = {Cameron Foreman and Richie Yeung and Florian J. Curchod},
      title = {Statistical testing of random number generators and their improvement using randomness extraction},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/492},
      year = {2024},
      doi = {10.3390/e26121053},
      url = {https://eprint.iacr.org/2024/492}
}