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arXiv 1401.3307cs.CR
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关于(伪)随机发生器的LIL检验的设计及一些实验结果

On the Design of LIL Tests for (Pseudo) Random Generators and Some Experimental Results

Yongge Wang

摘要:

NIST SP 800 -22(2010)提出了(伪)随机发生器的最新测试套件,以检测二进制序列与随机性的偏差。一方面,作为NIST SP 800 -22测试集的反例,很容易构造出NIST SP 800 -22测试集认为是GOOD伪随机发生器的函数,尽管这些函数的输出很容易与均匀分布区分开。因此,这些函数根据定义不是伪随机生成器。另一方面,NIST SP 800 -22没有涵盖随机性的一些重要定律。关于随机二元串的两个基本极限定理是中心极限定理和重对数律。NIST SP 800 -22中的几个频率相关测试涵盖了中心极限定理,而NIST SP 800 -22中没有测试涵盖LIL。本文提出了解决NIST SP 800 -22测试套件面临的上述挑战的技术。首先,我们提出了基于统计距离的(伪)随机发生器的测试技术,以减少上述NIST SP 800 -22测试套件中的第二类错误。其次,我们提出了基于LIL的统计测试技术,计算概率,并通过产生约30 TB的伪随机序列对广泛使用的伪随机发生器进行实验测试。实验结果表明,对于1000个序列(2 TB)的样本量,生成的序列与均匀分布之间的统计距离约为0.07($0$表示统计上不可区分,$1$表示完全可区分),均方根偏差约为0.005。

英文摘要:

NIST SP800-22 (2010) proposes the state of art testing suite for (pseudo) random generators to detect deviations of a binary sequence from randomness. On the one hand, as a counter example to NIST SP800-22 test suite, it is easy to construct functions that are considered as GOOD pseudorandom generators by NIST SP800-22 test suite though the output of these functions are easily distinguishable from the uniform distribution. Thus these functions are not pseudorandom generators by definition. On the other hand, NIST SP800-22 does not cover some of the important laws for randomness. Two fundamental limit theorems about random binary strings are the central limit theorem and the law of the iterated logarithm (LIL). Several frequency related tests in NIST SP800-22 cover the central limit theorem while no NIST SP800-22 test covers LIL. This paper proposes techniques to address the above challenges that NIST SP800-22 testing suite faces. Firstly, we propose statistical distance based testing techniques for (pseudo) random generators to reduce the above mentioned Type II errors in NIST SP800-22 test suite. Secondly, we propose LIL based statistical testing techniques, calculate the probabilities, and carry out experimental tests on widely used pseudorandom generators by generating around 30TB of pseudorandom sequences. The experimental results show that for a sample size of 1000 sequences (2TB), the statistical distance between the generated sequences and the uniform distribution is around 0.07 (with $0$ for statistically indistinguishable and $1$ for completely distinguishable) and the root-mean-square deviation is around 0.005.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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