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

• URL: http://arxiv.org/abs/2403.18716v2
• Date: Thu, 09 Jan 2025 11:08:52 GMT
• Title: Statistical testing of random number generators and their improvement using randomness extraction
• Authors: Cameron Foreman, Richie Yeung, Florian J. Curchod,
• Abstract summary: Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. We design, implement, and present various post-processing methods, using randomness extractors, to improve the RNG output quality. We introduce a comprehensive statistical testing environment, based on existing test suites, that can be parametrised for lightweight (fast) to intensive testing.
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• 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.

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