UNDERSTANDING RANDOMNESS IN COMPUTING: TRUE AND PSEUDO-RANDOM NUMBER GENERATORS

Авторы

  • Nuriddin Safoev Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan Автор
  • Sirojiddin Salimov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan Автор
  • Nafisa Yuldasheva Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan Автор
  • Ma’mur Murodov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan Автор
  • Boburjon Fayziraxmonov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan Автор

Ключевые слова:

Random number generation, pseudo-random number generation, entropy, cryptography, simulations.

Аннотация

Random number generation is a critical building block in computer science, underpinning applications in cryptography, simulations, secure communications, and statistical modeling. This paper presents a comprehensive overview of both true random number generators (TRNGs) and pseudo-random number generators (PRNGs)—two fundamentally different approaches to generating randomness. TRNGs rely on inherently unpredictable physical processes, such as thermal noise, radioactive decay, or quantum phenomena, to produce non-deterministic output. In contrast, PRNGs employ algorithmic methods that, while deterministic and reproducible, aim to simulate randomness from an initial seed value.

Библиографические ссылки

Chan, W. K. (2009). Random Number Generation in Simulation.

Gutterman, Z., Pinkas, B., & Reinman, T. (2006). Analysis of the Linux Random Number Generator.

Haahr, M. (2011). Introduction to Randomness and Random Numbers.

Marsaglia, G. (2005). Random Number Generators.

Schneier, B. (2007). Dual_EC_DRBG: A Case Study in Backdoors.

Sunar, B., Martin, W., & Stinson, D. (2006). A Provably Secure True Random Number Generator.

Barker, E., & Kelsey, J. (2015). Recommendation for Random Number Generation Using Deterministic Random Bit Generators (Revised). NIST Special Publication 800-90A Rev. 1. https://doi.org/10.6028/NIST.SP.800-90Ar1

Eastlake, D., Schiller, J., & Crocker, S. (2005). Randomness Requirements for Security. RFC 4086. https://www.rfc-editor.org/rfc/rfc4086

Microsoft. (2023). Cryptography API: Next Generation. Microsoft Docs. https://learn.microsoft.com/en-us/windows/win32/seccng/cng-portal

Microsoft. (2023). BCryptGenRandom function (bcrypt.h). Microsoft Docs. https://learn.microsoft.com/en-us/windows/win32/api/bcrypt/nf-bcrypt-bcryptgenrandom

Linux Kernel Documentation. (2023). Random Number Generator. https://www.kernel.org/doc/html/latest/admin-guide/dev-random.html

Linux man-pages project. (2023). getrandom(2) – Linux manual page. https://man7.org/linux/man-pages/man2/getrandom.2.html

Apple Developer Documentation. (2023). SecRandomCopyBytes. https://developer.apple.com/documentation/security/1399291-secrandomcopybytes

Apple. (2020). Platform Security Guide. https://support.apple.com/guide/security/welcome/web

Gutterman, Z., Pinkas, B., & Reinman, T. (2006). Analysis of the Linux Random Number Generator. IEEE Symposium on Security and Privacy. https://doi.org/10.1109/SP.2006.26

Dorrendorf, L., Gutterman, Z., & Pinkas, B. (2007). Cryptanalysis of the Random Number Generator of the Windows Operating System. ACM CCS. https://doi.org/10.1145/1315245.1315274

Lacharme, P. (2012). Security flaws in Linux's /dev/random. https://eprint.iacr.org/2012/251

BSD Unix. (2022). arc4random and related APIs. https://man.openbsd.org/arc4random

Kelsey, J., Schneier, B., Ferguson, N. (1999). Yarrow-160: Notes on the Design and Analysis of the Yarrow Cryptographic Pseudorandom Number Generator. https://www.schneier.com/paper-yarrow.pdf

Dodis, Y., et al. (2013). Security Analysis of Pseudorandom Number Generators with Input: /dev/random is not Robust. ACM CCS. https://doi.org/10.1145/2508859.2516661

Intel Corporation. (2014). Intel® Digital Random Number Generator (DRNG) Software Implementation Guide. https://www.intel.com/content/www/us/en/content-details/671488/intel-digital-random-number-generator-drng-software-implementation-guide.html

National Institute of Standards and Technology. (2012). A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. NIST SP 800-22 Rev. 1a. https://doi.org/10.6028/NIST.SP.800-22r1a

Müller, T. (2013). Security of the OpenSSL PRNG. International Journal of Information Security, 12(4), 251–265. https://doi.org/10.1007/s10207-013-0213-7

Debian Security Advisory. (2008). Debian OpenSSL Predictable PRNG Vulnerability (DSA-1571). https://www.debian.org/security/2008/dsa-1571

Опубликован

2025-06-10

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Как цитировать

UNDERSTANDING RANDOMNESS IN COMPUTING: TRUE AND PSEUDO-RANDOM NUMBER GENERATORS. (2025). Наука и инновации, 3(21), 129-135. https://in-academy.uz/index.php/SI/article/view/33813