Li Shouliang, Lin Zichen, Yang Yi, Ning Ruixuan
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China.
Entropy (Basel). 2024 Aug 7;26(8):671. doi: 10.3390/e26080671.
Pseudo-random number generators (PRNGs) are important cornerstones of many fields, such as statistical analysis and cryptography, and the need for PRNGs for information security (in fields such as blockchain, big data, and artificial intelligence) is becoming increasingly prominent, resulting in a steadily growing demand for high-speed, high-quality random number generators. To meet this demand, the multiple deep-dynamic transformation (MDDT) algorithm is innovatively developed. This algorithm is incorporated into the skewed tent map, endowing it with more complex dynamical properties. The improved one-dimensional discrete chaotic mapping method is effectively realized on a field-programmable gate array (FPGA), specifically the Xilinx xc7k325tffg900-2 model. The proposed pseudo-random number generator (PRNG) successfully passes all evaluations of the National Institute of Standards and Technology (NIST) SP800-22, diehard, and TestU01 test suites. Additional experimental results show that the PRNG, possessing high novelty performance, operates efficiently at a clock frequency of 150 MHz, achieving a maximum throughput of 14.4 Gbps. This performance not only surpasses that of most related studies but also makes it exceptionally suitable for embedded applications.
伪随机数生成器（PRNG）是许多领域的重要基石，如统计分析和密码学，并且在信息安全领域（如区块链、大数据和人工智能）对PRNG的需求日益突出，导致对高速、高质量随机数生成器的需求稳步增长。为满足这一需求，创新性地开发了多重深度动态变换（MDDT）算法。该算法被并入偏斜帐篷映射，赋予其更复杂的动力学特性。改进的一维离散混沌映射方法在现场可编程门阵列（FPGA）上，具体是赛灵思xc7k325tffg900 - 2型号上得到有效实现。所提出的伪随机数生成器（PRNG）成功通过了美国国家标准与技术研究院（NIST）SP800 - 22、diehard和TestU01测试套件的所有评估。额外的实验结果表明，该PRNG具有很高的新颖性，在150 MHz的时钟频率下高效运行，实现了14.4 Gbps的最大吞吐量。这一性能不仅超过了大多数相关研究，而且使其特别适用于嵌入式应用。
