Random Number Generators: Principles and Applications

Author:

Bikos Anastasios12ORCID,Nastou Panagiotis E.34,Petroudis Georgios3,Stamatiou Yannis C.15

Affiliation:

1. Computer Technology Institute and Press “Diophantus”, University of Patras Campus, 26504 Patras, Greece

2. Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece

3. Applied Mathematics and Mathematical Modeling Laboratory, Department of Mathematics, University of the Aegean, 83200 Samos, Greece

4. Center for Applied Optimization, University of Florida, Gainesville, FL 32611, USA

5. Department of Business Administration, University of Patras, 26504 Patras, Greece

Abstract

In this paper, we present approaches to generating random numbers, along with potential applications. Rather than trying to provide extensive coverage of several techniques or algorithms that have appeared in the scientific literature, we focus on some representative approaches, presenting their workings and properties in detail. Our goal is to delineate their strengths and weaknesses, as well as their potential application domains, so that the reader can judge what would be the best approach for the application at hand, possibly a combination of the available approaches. For instance, a physical source of randomness can be used for the initial seed; then, suitable preprocessing can enhance its randomness; then, the output of preprocessing can feed different types of generators, e.g., a linear congruential generator, a cryptographically secure one and one based on the combination of one-way hash functions and shared key cryptoalgorithms in various modes of operation. Then, if desired, the outputs of the different generators can be combined, giving the final random sequence. Moreover, we present a set of practical randomness tests that can be applied to the outputs of random number generators in order to assess their randomness characteristics. In order to demonstrate the importance of unpredictable random sequences, we present an application of cryptographically secure generators in domains where unpredictability is one of the major requirements, i.e., eLotteries and cryptographic key generation.

Publisher

MDPI AG

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Software

Reference37 articles.

1. Zenil, H. (2023, October 24). Available online: https://www.worldscientific.com/worldscibooks/10.1142/7973#t=toc.

2. Killmann, W., and Schindler, W. (, January September). A Proposal for Functionality Classes for Random Number Generators. Proceedings of the Bundesamt Sicherheit der Informationstechnik (BSI), Available online: https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/Certification/Interpretations/+AIS_31_Functionality_classes_for_random_number_generators_e.pdf?__blob=publicationFile&v=4.

3. Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Barker, E., Leigh, S., Levenson, M., Vangel, M., Banks, D., and Heckert, A. (2023, October 24). A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications, NIST Special Publication 800-22, Reveision 1a, Available online: https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-22r1a.pdf.

4. Turan, M.S., Barker, E., Kelsey, J., McKay, K.A., Baish, M.L., and Boyle, M. (2023, October 24). Recommendation for the Entropy Sources Used for Random Bit Generation, NIST Special Publication 800-90B. Available online: https://doi.org/10.6028/NIST.SP.800-90B.

5. Marsaglia, G. (2016, January 25). The Marsaglia Random Number CDROM including the Diehard Battery of Tests of Randomness. Available online: https://web.archive.org/web/20160125103112/http://stat.fsu.edu/pub/diehard/.

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