基于布尔混沌的物理随机数发生器

doi:10.11959/j.issn.1000-436x.2019014

Abstract

Abstract:

A novel method for generating physical random numbers using Boolean-chaos as the entropy source was proposed.An autonomous Boolean network (ABN) without self-feedback was constructed by using two-input logic gates,and its dynamic characteristics were analyzed.Based on this,a 15-node ABN circuit was implemented to successfully generate Boolean-chaos with a bandwidth of ~680 MHz and a min-entropy around 1.By implementing the entropy source and the entropy extraction circuit on a single FPGA,the physical random number generation with a real-time rate of 100 Mbit/s was finally achieved.The NIST SP800-22 and DIEHARD randomness test results demonstrate that the obtained random sequences by the method successfully pass all tests.This indicates the random numbers has good random statistical characteristics.

Key words: Boolean-chaos, autonomous Boolean network, physical random number generator, field programmable gate array

CLC Number:

TN918

Qiqi ZHANG,Jianguo ZHANG,Pu LI,Yanqiang GUO,Yuncai WANG. Boolean-chaos-based physical random number generator[J]. Journal on Communications, 2019, 40(1): 201-206.

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References 22

[1]	LI P , WANG Y C , WANG A B ,et al. Fast and tunable all-optical physical random number generator based on direct quantization of chaotic self-pulsations in two-section semiconductor lasers[J]. IEEE Journal of Selected Topics in Quantum Electronics, 2013,19(4):0600208.
[2]	GELENBE E , YIN Y . Deep learning with random neural networks[C]// International Joint Conference on Neural Networks, 2016: 1633-1638.
[3]	LI X , MA J , WANG W ,et al. A novel smart card and dynamic ID based remote user authentication scheme for multi-server environments[J]. Mathematical ＆ Computer Modelling, 2013,58(1-2): 85-95.
[4]	MATHEW S K , SRINIVASAN S , ANDERS M A ,et al. 2.4 Gbps,7 mW all-digital pvt-variation tolerant true random number generator for 45 nm cmos high-performance microprocessors[J]. IEEE Journal of Solid-State Circuits, 2012,47(11): 2807-2821.
[5]	PETRIE C S , CONNELLY A . A noise-based IC random number generator for applications in cryptography[J]. IEEE Transactions on Circuits ＆ Systems I Fundamental Theory ＆ Applications, 2000,47(5): 615-621.
[6]	BUCCI M , GERMANI L , LUZZI R ,et al. A high-speed oscillator-based truly random number source for cryptographic applications on a smart card IC[J]. IEEE Transactions on Computers, 2003,52(4): 403-409.
[7]	CICEK I , PUSANE A E , DUNDAR G . A novel design method for discrete time chaos based true random number generators[J]. Integration the VLSI Journal, 2014,47(1): 38-47.
[8]	PARK M , RODGERS J C , LATHROP D P . True random number generation using CMOS Boolean chaotic oscillator[J]. Microelectronics Journal, 2015,46(12): 1364-1370.
[9]	WANG X Y , QIN X . A new pseudo-random number generator based on CML and chaotic iteration[J]. Nonlinear Dynamics, 2012,70(2): 1589-1592.
[10]	ZHANG Y Q , WANG X Y . A symmetric image encryption algorithm based on mixed linear-nonlinear coupled map lattice[J]. Information Sciences, 2014,273(8): 329-351.
[11]	WANG X Y , YU Q . A block encryption algorithm based on dynamic sequences of multiple chaotic systems[J]. Communications in Nonlinear Science ＆ Numerical Simulation, 2009,14(2): 574-581.
[12]	温贺平, 禹思敏, 吕金虎 . 基于hadoop大数据平台和无简并高维离散超混沌系统的加密算法[J]. 物理学报, 2017,66(23): 70-83.
	WEN H P , YU S M , LYU J H . Encryption algorithm based on hadoop and non-degenerate high-dimensional discrete hyperchaotic system[J]. Acta Physica Sinica, 2017,66(23): 70-83.
[13]	李孟婷, 赵泽茂 . 一种新的混沌伪随机序列生成方法[J]. 计算机应用研究, 2011,28(1): 341-344.
	LI M T , ZHAO Z M . New method to generate chaotic pseudo-random sequence[J]. Application Research of Computers, 2011,28(1): 341-344.
[14]	ROSIN D P , RONTANI D , GAUTHIER D J . Ultrafast physical generation of random numbers using hybrid Boolean networks[J]. Physical review.E,Statistical,Nonlinear,and Soft Matter Physics, 2013,87(4):040902.
[15]	CAVALCANTE H L , GAUTHIER D J , SOCOLAR J E ,et al. On the origin of chaos in autonomous Boolean networks[J]. Philosophical Transactions Mathematical Physical ＆ Engineering Sciences, 2009,368(1911): 495-513.
[16]	ROSIN D P , RONTANI D , GAUTHIER D J . Experiments on autonomous Boolean networks[J]. Chaos An Interdisciplinary Journal of Nonlinear Science, 2013,23(2):025102
[17]	ROSENSTEIN M T , COLLINS J J , LUCA C J D . A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D:Nonlinear Phenomena, 1993,65(1-2): 117-134.
[18]	YAO T L , LIU H F , XU J L ,et al. Estimating the largest Lyapunov exponent and noise level from chaotic time series[J]. Chaos An Interdisciplinary Journal of Nonlinear Science, 2012,22(3):043103.
[19]	FRAGA L G D L,TLELO-CUAUTLE E . Optimizing the maximum Lyapunov exponent and phase space portraits in multi-scroll chaotic oscillators[J]. Nonlinear Dynamics, 2014,76(2): 1503-1515.
[20]	INTEL. Cyclone V device overview[EB]. Santa Clara:Integrated Electronics Corporation, 2018.
[21]	RUKHIN A , SOTO J , NECHVATAL J ,et al. A statistical test suite for random and pseudorandom number generators for cryptographic applications[J]. Andrew Rukhin Juan Soto James Nechvatal Miles Smid Elaine, 2010,59(4): 2289-2297.
[22]	ALANI M M . Testing randomness in ciphertext of block-ciphers using DieHard tests[J]. International Journal of Computer Science and Network Security, 2010,10(4): 53-57.

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统计测试	P值	proportion	测试结果
频率测试	0.123 038	0.992	通过
块内频率测试	0.715 679	0.985	通过
累加和测试	0.452 173	0.994	通过
游程测试	0.274 341	0.996	通过
块内长游程测试	0.311 542	0.986	通过
二进制矩阵秩测试	0.486 588	0.990	通过
离散傅里叶变换测试	0.101 311	0.983	通过
非重叠模块匹配测试	0.626 709	0.994	通过
重叠块比配测试	0.568 739	0.988	通过
全局通用统计测试	0.823 725	0.988	通过
近似熵测试	0.429 923	0.992	通过
随机游动测试	0.660 048	0.991	通过
随机游动变量测试	0.624 768	0.995	通过
串行测试	0.622 546	0.988	通过
线性复杂度测试	0.304 126	0.994	通过

统计测试	P值	测试结果
生日间隔检验	0.742 382	通过(KS)
重叠置换检验	0.931 053	通过
31×31二元矩阵秩检验	0.743 923	通过
32×32二元矩阵秩检验	0.924 107	通过
6×8二元矩阵秩检验	0.595 180	通过(KS)
比特流检验	0.179 66	通过
OPSO检验	0.942 3	通过
OQSO检验	0.924 0	通过
DNA检验	0.173 1	通过
比特1计数检验	0.376 065	通过
特定字节比特1计数检验	0.952 819	通过
泊车检验	0.189 101	通过(KS)
最小距离检验	0.974 863	通过(KS)
三维圆球检验	0.342 057	通过(KS)
减数检验	0.925 889	通过
重叠累计和检验	0.478 587	通过(KS)
游程检验	0.282 636	通过(KS)
掷骰检验	0.337 318	通过

Boolean-chaos-based physical random number generator

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Figures/Tables 7

References 22

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