True random number generators (TRNG) are important counterparts to pseudorandom number generators (PRNG), especially for high security applications such as cryptography. They produce unpredictable, non-repeatable random sequences. However, most TRNGs require specialized hardware to extract entropy from physical phenomena and tend to be slower than PRNGs. These generators usually require post-processing algorithms to eliminate biases but in turn, reduces performance. In this paper, a new post-processing method based on hyperchaos is proposed for software-based TRNGs which not only eliminates statistical biases but also provides amplification in order to improve the performance of TRNGs. The proposed method utilizes the inherent characteristics of chaos such as hypersensitivity to input changes, diffusion, and confusion capabilities to achieve these goals. Quantized bits of a physical entropy source are used to perturb the parameters of a hyperchaotic map, which is then iterated to produce a set of random output bits. To depict the feasibility of the proposed post-processing algorithm, it is applied in designing TRNGs based on digital audio. The generators are analyzed to identify statistical defects in addition to forward and backward security. Results indicate that the proposed generators are able to produce secure true random sequences at a high throughput, which in turn reflects on the effectiveness of the proposed post-processing method.

中文翻译：

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基于超混沌的真随机数发生器的后处理方法及其在基于音频的发生器中的应用

真正的随机数生成器（TRNG）是伪随机数生成器（PRNG）的重要副本，特别是对于加密等高安全性应用而言。它们产生不可预测的，不可重复的随机序列。但是，大多数TRNG需要专用硬件来从物理现象中提取熵，并且往往比PRNG慢。这些生成器通常需要后处理算法来消除偏差，但反过来会降低性能。针对基于软件的TRNG，提出了一种基于超混沌的后处理新方法，该方法不仅可以消除统计偏差，而且可以提供放大效果，以提高TRNG的性能。所提出的方法利用了混沌的固有特性，例如对输入变化，扩散，以及实现这些目标的困惑能力。物理熵源的量化位用于扰动超混沌映射的参数，然后对其进行迭代以生成一组随机输出位。为了描述所提出的后处理算法的可行性，将其应用于基于数字音频的TRNG设计中。除了前向和后向安全性之外，还对生成器进行分析以识别统计缺陷。结果表明，提出的生成器能够以高吞吐量产生安全的真实随机序列，这反过来又反映了提出的后处理方法的有效性。为了描述所提出的后处理算法的可行性，将其应用于基于数字音频的TRNG设计中。除了前向和后向安全性之外，还对生成器进行分析以识别统计缺陷。结果表明，提出的生成器能够以高吞吐量产生安全的真实随机序列，这反过来又反映了提出的后处理方法的有效性。为了描述所提出的后处理算法的可行性，将其应用于基于数字音频的TRNG设计中。除了前向和后向安全性之外，还对生成器进行分析以识别统计缺陷。结果表明，提出的生成器能够以高吞吐量产生安全的真实随机序列，这反过来又反映了提出的后处理方法的有效性。