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Journal articles on the topic 'Random number generators Random number generators'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Marsaglia, George. "Random Number Generators." Journal of Modern Applied Statistical Methods 2, no. 1 (May 1, 2003): 2–13. http://dx.doi.org/10.22237/jmasm/1051747320.

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2

Szczepanski, J., E. Wajnryb, J. M. Amigó, Maria V. Sanchez-Vives, and M. Slater. "Biometric random number generators." Computers & Security 23, no. 1 (February 2004): 77–84. http://dx.doi.org/10.1016/s0167-4048(04)00064-1.

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3

Dwyer, Gerald P., and K. B. Williams. "Portable random number generators." Journal of Economic Dynamics and Control 27, no. 4 (February 2003): 645–50. http://dx.doi.org/10.1016/s0165-1889(01)00065-3.

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4

Press, William H., and Saul A. Teukolsky. "Portable Random Number Generators." Computers in Physics 6, no. 5 (1992): 522. http://dx.doi.org/10.1063/1.4823101.

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5

Thomas, David B., Wayne Luk, Philip H. W. Leong, and John D. Villasenor. "Gaussian random number generators." ACM Computing Surveys 39, no. 4 (November 2, 2007): 11. http://dx.doi.org/10.1145/1287620.1287622.

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6

Collings, Bruce Jay. "Compound Random Number Generators." Journal of the American Statistical Association 82, no. 398 (June 1987): 525–27. http://dx.doi.org/10.1080/01621459.1987.10478457.

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7

Bastos, Daniel Chicayban, Luis Antonio Brasil Kowada, and Raphael C. S. Machado. "On pseudorandom number generators." ACTA IMEKO 9, no. 4 (December 17, 2020): 128. http://dx.doi.org/10.21014/acta_imeko.v9i4.730.

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<p class="Abstract">Statistical sampling and simulations produced by algorithms require fast random number generators; however, true random number generators are often too slow for the purpose, so pseudorandom number generators are usually more suitable. But choosing and using a pseudorandom number generator is no simple task; most pseudorandom number generators fail statistical tests. Default pseudorandom number generators offered by programming languages usually do not offer sufficient statistical properties. Testing random number generators so as to choose one for a project is essential to know its limitations and decide whether the choice fits the project’s objectives. However, this study presents a reproducible experiment that demonstrates that, despite all the contributions it made when it was first published, the popular NIST SP 800-22 statistical test suite as implemented in the software package is inadequate for testing generators.</p>
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8

Figotin, A., A. Gordon, J. Quinn, N. Stavrakas, and S. Molchanov. "Occupancy Numbers in Testing Random Number Generators." SIAM Journal on Applied Mathematics 62, no. 6 (January 2002): 1980–2011. http://dx.doi.org/10.1137/s0036139900366869.

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9

YAO, WEIGUANG, PEI YU, and CHRISTOPHER ESSEX. "COMMUNICATION BETWEEN SYNCHRONIZED RANDOM NUMBER GENERATORS." International Journal of Bifurcation and Chaos 14, no. 11 (November 2004): 3995–4008. http://dx.doi.org/10.1142/s0218127404011685.

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In most published chaos-based communication schemes, the system's parameters used as a key could be intelligently estimated by a cracker based on the fact that information about the key is contained in the chaotic carrier. In this paper, we will show that the least significant digits (LSDs) of a signal from a chaotic system can be so highly random that the system can be used as a random number generator. Secure communication could be built between the synchronized generators nonetheless. The Lorenz system is used as an illustration.
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10

N., H., and Istvan Deak. "Random Number Generators and Simulation." Mathematics of Computation 60, no. 201 (January 1993): 442. http://dx.doi.org/10.2307/2153189.

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11

Larrondo, H. A., M. T. Martín, C. M. González, A. Plastino, and O. A. Rosso. "Random number generators and causality." Physics Letters A 352, no. 4-5 (April 2006): 421–25. http://dx.doi.org/10.1016/j.physleta.2005.12.009.

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12

Collings, Bruce Jay, and Istvan Deak. "Random Number Generators and Simulation." Journal of the American Statistical Association 86, no. 416 (December 1991): 1143. http://dx.doi.org/10.2307/2290542.

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13

Panneton, Francois, and Pierre L’Ecuyer. "Resolution-stationary random number generators." Mathematics and Computers in Simulation 80, no. 6 (February 2010): 1096–103. http://dx.doi.org/10.1016/j.matcom.2007.09.014.

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14

Burford, Roger L. "Random number generators for microcomputers." Communications in Statistics - Simulation and Computation 19, no. 2 (January 1990): 649–62. http://dx.doi.org/10.1080/03610919008812880.

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15

Srinivasan, Ashok, Michael Mascagni, and David Ceperley. "Testing parallel random number generators." Parallel Computing 29, no. 1 (January 2003): 69–94. http://dx.doi.org/10.1016/s0167-8191(02)00163-1.

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16

Warford, J. S. "Good pedagogical random number generators." ACM SIGCSE Bulletin 24, no. 1 (March 1992): 142–46. http://dx.doi.org/10.1145/135250.134539.

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17

Marsaglia, George. "Seeds for random number generators." Communications of the ACM 46, no. 5 (May 2003): 90–93. http://dx.doi.org/10.1145/769800.769827.

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18

Herring, C., and J. I. Palmore. "Random number generators are chaotic." ACM SIGPLAN Notices 24, no. 11 (November 1989): 76–79. http://dx.doi.org/10.1145/71605.71608.

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19

Herring, Charles, and Julian I. Palmore. "Random number generators are chaotic." Communications of the ACM 38, no. 1 (January 2, 1995): 121–22. http://dx.doi.org/10.1145/204865.204895.

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20

Chen, Sophia. "Random number generators go public." Science 360, no. 6396 (June 28, 2018): 1383–84. http://dx.doi.org/10.1126/science.360.6396.1383.

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21

Fisher, Joseph A. "Object oriented random number generators." Computers & Industrial Engineering 25, no. 1-4 (September 1993): 561–63. http://dx.doi.org/10.1016/0360-8352(93)90344-w.

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22

Bowman, Richard L. "Evaluating pseudo-random number generators." Computers & Graphics 19, no. 2 (March 1995): 315–24. http://dx.doi.org/10.1016/0097-8493(94)00158-u.

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23

Andrecut, M. "Logistic Map as a Random Number Generator." International Journal of Modern Physics B 12, no. 09 (April 10, 1998): 921–30. http://dx.doi.org/10.1142/s021797929800051x.

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For the largest value of the control parameter, the logistic map is able to generate an infinite chaotic sequence of numbers. Here we describe a simple method for obtaining a random number generator based on this property of the logistic map. Comparing to usual congruential random generators, which are periodic, the logistic random number generator is infinite, aperiodic and not correlated. An aperiodic random number generator is a valuable tool for computer simulation methods.
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24

Sulewski, Piotr. "Comparison of normal random number generators." Wiadomości Statystyczne. The Polish Statistician 64, no. 7 (July 29, 2019): 5–31. http://dx.doi.org/10.5604/01.3001.0013.7605.

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The sampling in statistical surveys and numerical calculations as well as simulation testing of probabilistic models in virtually all fields of knowledge require a com-puter endowed with pseudorandom numbers generators. The main goal of the study is to compare the normal random number generators using various criteria. The properties of 12 random number generators for a normal distribution were investigated. Then, the family of generators was extended by two so-called application generators and a new approach for checking the quality of generators was adopted. A ready-made tool pre-pared in C++ and in Visual Basic for Application (VBA) for conducting self-contained research using generators was presented. All Monte Carlo simulations were carried out in C++, while the calculations were performed in the VBA editor using the Microsoft Excel 2016 spreadsheet. The analysis of the obtained results shows that the generators with best properties are: MP Monty Python, R, Biegun and Ziggurat. The worst generators, are: BM Box-Muller, Wallace, Iloraz and Excel.
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25

Pikuza, M. O., and S. Yu Mikhnevich. "Testing a hardware random number generator using NIST statistical test suite." Doklady BGUIR 19, no. 4 (July 1, 2021): 37–42. http://dx.doi.org/10.35596/1729-7648-2021-19-4-37-42.

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Random number generators are required for the operation of cryptographic information protection systems. For а correct application of the generator in the field of information security, it is necessary that its output sequence to be indistinguishable from a uniformly distributed random sequence. To verify this, it is necessary to test the generator output sequence using various statistical test suites such as Dihard and NIST. The purpose of this work is to test a prototype hardware random number generator. The generator is built on the basis of the ND103L noise diode and has a random digital sequence of binary numbers at the output. In the prototype there is a possibility of regulating the amount of reverse current through the noise diode, as well as setting the data acquisition period, i.e. data generation frequency. In the course of operation, a number of sequences of random numbers were removed from the generator at various values of the reverse current through the noise diode, the period of data acquisition and the ambient temperature. The resulting sequences were tested using the NIST statistical test suite. After analyzing the test results, it was concluded that the generator operates relatively stably in a certain range of initial parameters, while the deterioration in the quality of the generator's operation outside this range is associated with the technical characteristics of the noise diode. It was also concluded that the generator under study is applicable in certain applications and to improve the stability of its operation, it can be improved both in hardware and software. The results of this work can be useful to developers of hardware random number generators built according to a similar scheme.
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26

Korchynskyi, Volodymyr, Vitalii Kildishev, Oleksandr Riabukha, and Oleksandr Berdnikov. "THE GENERATING RANDOM SEQUENCES WITH THE INCREASED CRYPTOGRAPHIC STRENGTH." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 1 (March 30, 2020): 20–23. http://dx.doi.org/10.35784/iapgos.916.

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Random sequences are used in various applications in construction of cryptographic systems or formations of noise-type signals. For these tasks there is used the program generator of random sequences which is the determined device. Such a generator, as a rule, has special requirements concerning the quality of the numbers formation sequence. In cryptographic systems, the most often used are linearly – congruent generators, the main disadvantage of which is the short period of formation of pseudo-random number sequences. For this reason, in the article there is proposed the use of chaos generators as the period of the formed selection in this case depends on the size of digit net of the used computing system. It is obvious that the quality of the chaos generator has to be estimated through a system of the NIST tests. Therefore, detailed assessment of their statistical characteristics is necessary for practical application of chaos generators in cryptographic systems. In the article there are considered various generators and there is also given the qualitative assessment of the formation based on the binary random sequence. Considered are also the features of testing random number generators using the system. It is determined that not all chaos generators meet the requirements of the NIST tests. The article proposed the methods for improving statistical properties of chaos generators. The method of comparative analysis of random number generators based on NIST statistical tests is proposed, which allows to select generators with the best statistical properties. Proposed are also methods for improving the statistical characteristics of binary sequences, which are formed on the basis of various chaos generators.
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27

Zhurbenko, I. G., and O. S. Smirnova. "Some properties of random number generators." Journal of Soviet Mathematics 47, no. 5 (December 1989): 2703–7. http://dx.doi.org/10.1007/bf01095595.

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28

Visweswariah, K., S. R. Kulkarni, and S. Verdu. "Source codes as random number generators." IEEE Transactions on Information Theory 44, no. 2 (March 1998): 462–71. http://dx.doi.org/10.1109/18.661497.

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29

Compagner, A., A. S. Berdnikov, S. B. Turtia, and A. Larionov. "Rounding errors in random number generators." Computer Physics Communications 106, no. 3 (November 1997): 207–18. http://dx.doi.org/10.1016/s0010-4655(97)00070-2.

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30

Deng, Lih-Yuan, and Dale Bowman. "Developments in pseudo-random number generators." Wiley Interdisciplinary Reviews: Computational Statistics 9, no. 5 (August 8, 2017): e1404. http://dx.doi.org/10.1002/wics.1404.

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31

Martin, Clyde F., and Mara D. Neusel. "Invariants of pseudo-random number generators." Communications in Information and Systems 8, no. 1 (2008): 39–54. http://dx.doi.org/10.4310/cis.2008.v8.n1.a3.

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32

L'Ecuyer, Pierre. "Combined Multiple Recursive Random Number Generators." Operations Research 44, no. 5 (October 1996): 816–22. http://dx.doi.org/10.1287/opre.44.5.816.

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33

James, F. "More Light on Random Number Generators." Europhysics News 24, no. 7 (1993): 183. http://dx.doi.org/10.1051/epn/19932407183.

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34

Chiang, Kao, and J. Y. Wong. "Several extensively tested random number generators." Computers & Operations Research 21, no. 9 (November 1994): 1035–39. http://dx.doi.org/10.1016/0305-0548(94)90074-4.

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35

Wu, J., and M. O'Neill. "Ultra-lightweight true random number generators." Electronics Letters 46, no. 14 (2010): 988. http://dx.doi.org/10.1049/el.2010.0893.

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36

Deng, Lih-Yuan, Kwok Hung Chan, and Yilian Yuan. "Random Number Generators For Multiprocessor Systems." International Journal of Modelling and Simulation 14, no. 4 (January 1994): 185–91. http://dx.doi.org/10.1080/02286203.1994.11760238.

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37

Panneton, François, and Pierre L'Ecuyer. "On the xorshift random number generators." ACM Transactions on Modeling and Computer Simulation 15, no. 4 (October 2005): 346–61. http://dx.doi.org/10.1145/1113316.1113319.

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38

Polack, Jean‐Dominique. "Are concert halls random number generators?" Journal of the Acoustical Society of America 120, no. 5 (November 2006): 3101. http://dx.doi.org/10.1121/1.4787547.

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39

Pesonen, Martti I. "Random number generators for compound distributions." Scandinavian Actuarial Journal 1989, no. 1 (January 1989): 47–60. http://dx.doi.org/10.1080/03461238.1989.10413854.

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40

Marsaglia, G., and A. Zaman. "Monkey tests for random number generators." Computers & Mathematics with Applications 26, no. 9 (November 1993): 1–10. http://dx.doi.org/10.1016/0898-1221(93)90001-c.

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41

Rezk, Ahmed A., Ahmed H. Madian, Ahmed G. Radwan, and Ahmed M. Soliman. "Multiplierless chaotic Pseudo random number generators." AEU - International Journal of Electronics and Communications 113 (January 2020): 152947. http://dx.doi.org/10.1016/j.aeue.2019.152947.

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42

Tang, Hui-Chin. "Reverse multiple recursive random number generators." European Journal of Operational Research 164, no. 2 (July 2005): 402–5. http://dx.doi.org/10.1016/j.ejor.2003.10.047.

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43

Micali, S., and C. P. Schnorr. "Efficient, perfect polynomial random number generators." Journal of Cryptology 3, no. 3 (January 1991): 157–72. http://dx.doi.org/10.1007/bf00196909.

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44

Eddy, William F. "Random number generators for parallel processors." Journal of Computational and Applied Mathematics 31, no. 1 (July 1990): 63–71. http://dx.doi.org/10.1016/0377-0427(90)90336-x.

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45

Gentle, James E. "Computer implementation of random number generators." Journal of Computational and Applied Mathematics 31, no. 1 (July 1990): 119–25. http://dx.doi.org/10.1016/0377-0427(90)90342-w.

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46

Martínez, Aldo, Aldo Solis, Rafael Díaz Hernández Rojas, Alfred U'Ren, Jorge Hirsch, and Isaac Pérez Castillo. "Advanced Statistical Testing of Quantum Random Number Generators." Entropy 20, no. 11 (November 17, 2018): 886. http://dx.doi.org/10.3390/e20110886.

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Pseudo-random number generators are widely used in many branches of science, mainly in applications related to Monte Carlo methods, although they are deterministic in design and, therefore, unsuitable for tackling fundamental problems in security and cryptography. The natural laws of the microscopic realm provide a fairly simple method to generate non-deterministic sequences of random numbers, based on measurements of quantum states. In practice, however, the experimental devices on which quantum random number generators are based are often unable to pass some tests of randomness. In this review, we briefly discuss two such tests, point out the challenges that we have encountered in experimental implementations and finally present a fairly simple method that successfully generates non-deterministic maximally random sequences.
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47

Datcu, Octaviana, Corina Macovei, and Radu Hobincu. "Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change." Applied Sciences 10, no. 2 (January 8, 2020): 451. http://dx.doi.org/10.3390/app10020451.

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This article presents a configurable, high-throughput pseudo-random number generator template targeting cryptographic applications. The template is parameterized using a chaotic map that generates data, an entropy builder that is used to periodically change the parameters of the map and a parameter change interval, which is the number of iterations after which the entropy builder will change the generator’s parameters. The system is implemented in C++ and evaluated using the TestU01 and NIST RNG statistical tests. The same implementation is used for a stream cipher that can encrypt and decrypt PNG images. A Monte-Carlo analysis of the seed space was performed. Results show that for certain combinations of maps and entropy builders, more than 90% of initial states (seeds) tested pass all statistical randomness tests. Also, the throughput is large enough so that a 8 K color image can be encrypted in 2 s on a modern laptop CPU (exact specifications are given in the paper). The conclusion is that chaotic maps can be successfully used as a building block for cryptographic random number generators.
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48

Brugger, C., S. Weithoffer, C. de Schryver, U. Wasenmüller, and N. Wehn. "On parallel random number generation for accelerating simulations of communication systems." Advances in Radio Science 12 (November 10, 2014): 75–81. http://dx.doi.org/10.5194/ars-12-75-2014.

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Abstract. Powerful compute clusters and multi-core systems have become widely available in research and industry nowadays. This boost in utilizable computational power tempts people to run compute-intensive tasks on those clusters, either for speed or accuracy reasons. Especially Monte Carlo simulations with their inherent parallelism promise very high speedups. Nevertheless, the quality of Monte Carlo simulations strongly depends on the quality of the employed random numbers. In this work we present a comprehensive analysis of state-of-the-art pseudo random number generators like the MT19937 or the WELL generator used for parallel stream generation in different settings. These random number generators can be realized in hardware as well as in software and help to accelerate the analysis (or simulation) of communications systems. We show that it is possible to generate high-quality parallel random number streams with both generators, as long as some configuration constraints are met. We furthermore depict that distributed simulations with those generator types are viable even to very high degrees of parallelism.
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49

Łoza, Szymon, Łukasz Matuszewski, and Mieczysław Jessa. "A Random Number Generator Using Ring Oscillators and SHA-256 as Post-Processing." International Journal of Electronics and Telecommunications 61, no. 2 (June 1, 2015): 199–204. http://dx.doi.org/10.1515/eletel-2015-0026.

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Abstract Today, cryptographic security depends primarily on having strong keys and keeping them secret. The keys should be produced by a reliable and robust to external manipulations generators of random numbers. To hamper different attacks, the generators should be implemented in the same chip as a cryptographic system using random numbers. It forces a designer to create a random number generator purely digitally. Unfortunately, the obtained sequences are biased and do not pass many statistical tests. Therefore an output of the random number generator has to be subjected to a transformation called post-processing. In this paper the hash function SHA-256 as post-processing of bits produced by a combined random bit generator using jitter observed in ring oscillators (ROs) is proposed. All components – the random number generator and the SHA-256, are implemented in a single Field Programmable Gate Array (FPGA). We expect that the proposed solution, implemented in the same FPGA together with a cryptographic system, is more attack-resistant owing to many sources of randomness with significantly different nominal frequencies.
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50

LUI, OI-YAN, CHING-HUNG YUEN, and KWOK-WO WONG. "A PSEUDO-RANDOM NUMBER GENERATOR EMPLOYING MULTIPLE RÉNYI MAPS." International Journal of Modern Physics C 24, no. 11 (October 14, 2013): 1350079. http://dx.doi.org/10.1142/s0129183113500794.

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The increasing risk along with the drastic development of multimedia data transmission has raised a big concern on data security. A good pseudo-random number generator is an essential tool in cryptography. In this paper, we propose a novel pseudo-random number generator based on the controlled combination of the outputs of several digitized chaotic Rényi maps. The generated pseudo-random sequences have passed both the NIST 800-22 Revision 1a and the DIEHARD tests. Moreover, simulation results show that the proposed pseudo-random number generator requires less operation time than existing generators and is highly sensitive to the seed.
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