Studies on the Computational Model of PRNG for Data Privacy Risk Mitigation in 5G Networks Sergiy Gnatyuk 1,2,3 , Dinara Ospanova 4 , Volodymyr Lytvynenko 5 , Zhazira Amirgaliyeva 6 and Nurali Nabot Shohiyon 7 1 Yessenov University, microdistrict 32, Main building, Aktau, 130000, Kazakhstan 2 National Aviation University, Liubomyra Huzara ave.1, Kyiv, 03058, Ukraine 3 Yessenov 3 State Scientific and Research Institute of Cybersecurity Technologies and Information Protection, Maksyma Zalizniaka str.3/6, Kyiv, 03142, Ukraine 4 Kazakh Humanitarian Juridical Innovative University, Lenin str, 11, Semey, 070000, Kazakhstan 5 Kherson National Technical University, Beryslavske shose 24, Kherson, 73008, Ukraine 6 Al-Farabi Kazakh National University, 71 al-Farabi Ave., Almaty, 050040, Kazakhstan 7 Dangara State University, Markazi str. 25, Dangara, 735320, Tajikistan Abstract Today, pseudo-random number generators (PRNGs) are used in various systems and applications, including as key generators in stream ciphers, blockchain, game industry and others. The implementation of the latest information and communication technology (in particular, modern 5G networks) strengthens the requirements for privacy risk mitigation of critical data and forces the development of new methods and means for cryptographic security. In the paper, a computational model of PRNG was developed and studied. It allows to build efficient algorithms for privacy risk mitigation. Based on this model, software PRNGs have been developed and studied (speed and security parameters were verified). These will be useful for confidentiality ensuring and data privacy risk mitigation in modern 5G networks as well as blockchain technologies. Keywords1 Computational Model, PRNG, Data Privacy, Risk, Algorithm, 5G Networks, Blockchain. 1. Introduction Today randomness is an important unit in many modern computer applications (especially games, simulations, cryptography). Computers use a form of randomness known as pseudo randomness, it means simulation of randomness. A pseudo random event looks random but is completely predictable or deterministic (result of completely predictable mathematical algorithm). Pseudo-random number generator (PRNG) can be used as key generator in stream ciphers [1] to form large key sequence with small input data of PRNG. This generator creates bit sequence similar to random sequence by statistical parameters. In practice, these sequences are CITRisk’2021: 2nd International Workshop on Computational & Information Technologies for Risk-Informed Systems, September 16–17, 2021, Kherson, Ukraine EMAIL: s.gnatyuk@yu.edu.kz (S.Gnatyuk); odm-1778@mail.ru (D.Ospanova); immun56@gmail.com (V.Lytvynenko); zh.amirgaliyeva@gmail.com (Z.Amirgaliyeva); shoev_n@list.ru (N.N.Shohiyon) ORCID: 0000-0003-4992-0564 (S.Gnatyuk); 0000-0002-2206-7367 (D.Ospanova); 0000-0002-1536-5542 (V.Lytvynenko); 0000- 0003-0484-8060 (Z.Amirgaliyeva); 0000-0003-2843-0945 (N.N.Shohiyon) © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)