Statistical testing of random number generators and their improvement using randomness extraction
<|reference_start|>Statistical testing of random number generators and their improvement using randomness extraction: Random number generators (RNGs) are notoriously hard to build and test, especially in a cryptographic setting. Although one cannot conclusively determine the quality of an RNG by testing the statistical properties of its output alone, running numerical tests is both a powerful verification tool and the only universally applicable method. In this work, we present and make available a comprehensive statistical testing environment (STE) that is based on existing statistical test suites. The STE can be parameterised to run lightweight (i.e. fast) all the way to intensive testing, which goes far beyond what is required by certification bodies. With it, we benchmark the statistical properties of several RNGs, comparing them against each other. We then present and implement a variety of post-processing methods, in the form of randomness extractors, which improve the RNG's output quality under different sets of assumptions and analyse their impact through numerical testing with the STE.<|reference_end|>
@article{foreman2024statistical,
    title={Statistical testing of random number generators and their improvement
  using randomness extraction},
    author={Cameron Foreman, Richie Yeung and Florian J. Curchod},
    journal={arXiv preprint arXiv:2403.18716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18716},
    primaryClass={cs.CR quant-ph}
}