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一种使用随机计算和逻辑斯谛映射的硬件伪随机数发生器。

A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map.

作者信息

Liu Junxiu, Liang Zhewei, Luo Yuling, Cao Lvchen, Zhang Shunsheng, Wang Yanhu, Yang Su

机构信息

School of Electronic Engineering, Guangxi Normal University, Guilin 541004, China.

Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China.

出版信息

Micromachines (Basel). 2020 Dec 30;12(1):31. doi: 10.3390/mi12010031.

Abstract

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

摘要

最近的研究表明,混沌映射被视为生成伪随机数的替代方法,并且已经针对相应的硬件实现提出了各种方法。在这项工作中,提出了一种高效的硬件伪随机数发生器(PRNG),其中通过使用扰动操作对一维逻辑斯谛映射进行了优化,该操作有效地减少了数字混沌的退化。通过采用随机计算,设计了一种硬件利用率相对较低的硬件PRNG。所提出的硬件PRNG是使用现场可编程门阵列器件实现的。结果表明,通过使用扰动操作,混沌映射实现了良好的安全性能,并且生成的伪随机数通过了TestU01测试和NIST SP 800-22测试。最重要的是,与传统方法相比,它还节省了89%的硬件资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7824605/99afe036efaf/micromachines-12-00031-g001.jpg

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