Foreman Cameron, Yeung Richie, Curchod Florian J
Quantinuum, Partnership House, Carlisle Place, London SW1P 1BX, UK.
Department of Computer Science, University College London, London WC1E 6BT, UK.
Entropy (Basel). 2024 Dec 4;26(12):1053. doi: 10.3390/e26121053.
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.
随机数生成器（RNG）的构建和测试极具挑战性，尤其是在密码学应用中。虽然统计测试不能绝对保证RNG的输出质量，但它们是一种强大的验证工具，也是唯一普遍适用的测试方法。在这项工作中，我们设计、实现并展示了各种使用随机性提取器的后处理方法，以提高RNG的输出质量，并通过统计测试对它们进行比较。我们首先对三个RNG——32位线性反馈移位寄存器（LFSR）、英特尔的“RDSEED”和IDQuantique的“Quantis”——进行密集测试，并比较它们的性能。接下来，我们将不同的后处理方法应用于每个RNG，并对处理后的输出进行进一步的密集测试。为便于进行此操作，我们基于现有测试套件引入了一个全面的统计测试环境，该环境可以针对轻量级（快速）到密集型测试进行参数化设置。
