Assessing the Statistical Quality of RNGs

Samyak Surti, Timothy J Proctor, Jordan Hines +1 more

Soheil Hajibaba, AmirHosein Dadahkhani, Hamid Asgari +2 more

As true random numbers are essential for many applications, high-quality Random Number Generators (RNGs) are highly demanded. Several different statistical test suites have been developed to evaluate the quality of random number generators. However, some programming skills are required to work with these statistical tests as they are not available as self-contained software and often are without GUI. In this work, we have developed PiRATe (Pi Randomness Assessment Test) an easy-to-use wrapper software for the assessment of random number generators using a comprehensive set of statistical tests, including DIEHARDER, NIST SP800-22, NIST SP800-90B, ENT, Borel Normality, and four Chaitin-Schwartz-Solovay-Strassen tests. We then used PiRATe to evaluate the quality of five different (Commercial and Experimental) Quantum Random Number Generators (QRNGs).

Cameron Foreman, Richie Wai Kit Yeung, Florian J. Curchod

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.

Stanley Gudder, Nino Zanghı̀

Václav Skřivánek, Roman Frič

Michael A. Wayne, Alan L. Migdall, Zachary H. Levine +1 more

The low-latency requirements of a loophole-free Bell test prohibit time-consuming post-processing steps that are often used to improve the statistical quality of a physical random number generator (RNG). Here we demonstrate a postprocessing-free RNG that produces a random bit within 2.4(2) ns of an input trigger. We use the Allan variance as a tool for characterizing non-idealities in the RNG and designing a feedback mechanism to account for and correct long-term drift. The impact of the feedback on the predictability of the output is less than 6.4 × 107 , and results in a system capable of 24 hour operation with output that is statistically indistinguishable from a balanced Bernoulli process.