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On the Entropy of Oscillator-Based True Random Number Generators

机译:基于振荡器的真正随机数发生器的熵

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True random number generators (TRNGs) are essential for cryptographic systems, and they are usually evaluated by the concept of entropy. In general, the entropy of a TRNG is estimated from its stochastic model, and reflected in the statistical results of the generated raw bits. Oscillator-based TRNGs are widely used in practical cryptographic systems due to its elegant structure, and its stochastic model has been studied in different aspects. In this paper, we investigate the applicability of the different entropy estimation methods for oscillator-based TRNGs, including the bit-rate entropy, the lower bound and the approximate entropy. Particularly, we firstly analyze the two existing stochastic models (one of which is phase-based and the other is time-based), and deduce consistent bit-rate entropy results from these two models. Then, we design an approximate entropy calculation method on the output raw bits of a simulated oscillator-based TRNG, and this statistical calculation result well matches the bit-rate entropy from stochastic models. In addition, we discuss the extreme case of tiny randomness where some methods are inapplicable, and provide the recommendations for these entropy evaluation methods. Finally, we design a hardware verification method in a real oscillator-based TRNG, and validate these estimation methods in the hardware platform.
机译:真正的随机数生成器(trngs)对于加密系统至关重要,并且通常由熵的概念评估。通常,TRNG的熵由其随机模型估计,并反映在所产生的原始比特的统计结果中。由于其优雅的结构,基于振荡器的TRNG广泛用于实用加密系统,并且在不同方面研究了随机模型。在本文中,我们调查了不同熵估计方法对基于振荡器的TNGS的适用性,包括比特率熵,下限和近似熵。特别是,我们首先分析了两种现有的随机模型(其中一个是基于相位的,另一个是时间为基础),并从这两个模型中推断出一致的比特率熵结果。然后,我们在基于模拟振荡器的TRNG的输出原始比特设计近似熵计算方法,并且这种统计计算结果良好地与随机模型的比特率熵匹配。此外,我们讨论了某些方法不适用的微小随机性的极端情况,并为这些熵评估方法提供了建议。最后,我们在基于真实振荡器的TRNG中设计硬件验证方法,并在硬件平台中验证这些估算方法。

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