作为RNG调节器的代码生成器矩阵

On asymptotically optimal tests for random number generators

The problem of constructing effective statistical tests for random number generators (RNG) is considered. Currently, statistical tests for RNGs are a mandatory part of cryptographic information protection systems, but their effectiveness is mainly estimated based on experiments with various RNGs. We find an asymptotic estimate for the p-value of an optimal test in the case where the alternative hypothesis is a known stationary ergodic source, and then describe a family of tests each of which has the same asymptotic estimate of the p-value for any (unknown) stationary ergodic source.

Calibrating random number generator tests

Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to $ \infty $). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes…

用于缩放随机数生成器的比特回收

Bit recycling for scaling random number generators

本文讨论将随机数生成器输出的均匀样本从固定比特范围缩放到可变范围M的方法，分析其数学效率和计算速度。

脉冲星随机数生成

Random Number Generation from Pulsars

本文利用脉冲星信号计时的细微变化作为自然熵源，结合随机性提取器构建随机数生成器，并通过熵建模与NIST测试验证其接近理想均匀随机源。

混沌映射真随机数发生器性能研究框架

A Framework for Investigating the Performance of Chaotic-Map Truly Random Number Generators

在本文中，我们近似的混沌映射真随机数发生器（TRNG）的隐马尔可夫模型，并描述其基本限制的基础上的近似熵率的比特生成过程。我们证明了熵率在混沌映射真随机数发生器的性能和鲁棒性中起着关键作用，这在电路设计优化中必须考虑。我们进一步推导出从原始RNG中提取真正随机位的后处理单元的最优性条件。

随机数产生的热力学

Thermodynamics of Random Number Generation

我们通过最近引入的信息处理第二定律分析了产生随机数的三种主要方法的热力学成本。给定对随机性的指定源的访问，随机数生成器（RNG）从期望的目标概率分布产生样本。这不同于使用完全确定性算法的伪随机数生成器（PRNG）和其中随机性源是物理系统的真随机数生成器（TRNG）。对于每个类，我们分析了热力学的发电机的基础上实现的算法作为有限状态机，因为这些允许所需的物理资源的直接边界。这建立了在三个主要类别的RNG算法-包括冯诺依曼，克努特和姚明，罗氏和Hoshi-和PRNG方法的操作过程中的散热和工作消耗的界限。我们介绍了一个一般的TRNG，并确定其热力学成本完全为任意的目标分布。结果突出了随机数生成的三种主要方法之间的显着差异：一种是工作产生，一种是工作消耗，另一种是潜在的耗散中性。值得注意的是，TRNG既可以生成随机数，又可以将热能转换为存储的功。这些信息创造的热力学成本补充了Landauer对信息破坏的不可减少成本的限制。