Liu Junxiu, Liang Zhewei, Luo Yuling, Cao Lvchen, Zhang Shunsheng, Wang Yanhu, Yang Su
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.
Micromachines (Basel). 2020 Dec 30;12(1):31. doi: 10.3390/mi12010031.
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.
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。
