Great expectations: A critique of current approaches to random number generation testing & certification

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Abstract

Random number generators are a critical component of security systems. They also find use in a variety of other applications from lotteries to scientific simulations. Randomness tests, such as the NIST’s STS battery (documented in SP800-22), Marsaglia’s Diehard, and L’Ecuyer et al.’s TestU01 seek to find whether a generator exhibits any signs of non-random behaviour. However, many statistical test batteries are unable to reliably detect certain issues present in poor generators. Severe mistakes when determining whether a given generator passes the tests are common. Irregularities in sample size selection and a lack of granularity in test result interpretation contribute to this. This work provides evidence of these and other issues in several statistical test batteries. We identify problems with current practices and recommend improvements. The novel concept of suitable randomness is presented, precisely defining two bias bounds for a TRNG, instead of a simple binary pass/fail outcome. Randomness naivety is also introduced, outlining how binary pass/fail analysis cannot express the complexities of RNG output in a manner that is useful to determine whether a generator is suitable for a given range of applications.

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APA

Hurley-Smith, D., & Hernandez-Castro, J. (2018). Great expectations: A critique of current approaches to random number generation testing & certification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11322 LNCS, pp. 143–163). Springer Verlag. https://doi.org/10.1007/978-3-030-04762-7_8

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