Cryptographically Secured Pseudo-Random Number Generators: Analysis and Testing with NIST Statistical Test Suite

Elena Almaraz Luengo () and Javier Román Villaizán
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Elena Almaraz Luengo: Department of Statistics and Operational Research, Faculty of Mathematical Science, Complutense University of Madrid, 28040 Madrid, Spain
Javier Román Villaizán: Faculty of Mathematical Science, Complutense University of Madrid, 28040 Madrid, Spain

Mathematics, 2023, vol. 11, issue 23, 1-31

Abstract: There are several areas of knowledge in which (pseudo-)random numbers are necessary, for example, in statistical–mathematical simulation or in cryptography and system security, among others. Depending on the area of application, it will be necessary that the sequences used meet certain requirements. In general, randomness and uniformity conditions are required in the generated sequences, which are checked with statistical tests, and conditions on sequence unpredictability if the application is in security. In the present work, a literature review on cryptographically secure pseudo-random number generators (CSPRNGs) is carried out, they are implemented, and a critical analysis of their statistical quality and computational efficiency is performed. For this purpose, different programming languages will be used, and the sequences obtained will be checked by means of the NIST Statistical Test Suite (NIST STS). In addition, a user’s guide will be provided to allow the selection of one generator over another according to its statistical properties and computational implementation characteristics.

Keywords: cryptographically secured pseudo-random number generators; hypothesis testing; NIST STS; test battery; test suite (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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