A Search for Good Pseudo-random Number Generators : Survey and Empirical Studies
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Abstract (arXiv)
This paper targets to search so-called good generators by doing a brief survey over the generators developed in the history of pseudo-random number generators (PRNGs), verify their claims and rank them based on strong empirical tests in same platforms. To do this, the genre of PRNGs developed so far are explored and classified into three groups -- linear congruential generator based, linear feedback shift register based and cellular automata based. From each group, the well-known widely used generators which claimed themselves to be `good' are chosen. Overall 30 PRNGs are selected in this way on which two types of empirical testing are done -- blind statistical tests with Diehard battery of tests, battery rabbit of TestU01 library and NIST statistical test-suite as well as graphical tests (lattice test and space-time diagram test). Finally, the selected PRNGs are divided into 24 groups and are ranked according to their overall performance in all empirical tests.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2018.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2.
4 papers.
- 2024SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generatorscited by 4
- 2022PiouCrypt: Decentralized Lattice-based Method for Visual Symmetric Cryptographycited by 0
- 2025Bias Variation Compensation in Perimeter-Gated SPAD TRNGscited by 0
- 2025AI-Hybrid TRNG: Kernel-Based Deep Learning for Near-Uniform Entropy Harvesting from Physical Noisecited by 0
- 2025AI-Hybrid TRNG: Kernel-Based Deep Learning for Near-Uniform Entropy Harvesting from Physical Noise30 Jun 2025
- 2025Bias Variation Compensation in Perimeter-Gated SPAD TRNGs18 Jun 2025
- 2024SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators14 Oct 2024
- 2022PiouCrypt: Decentralized Lattice-based Method for Visual Symmetric Cryptography17 Apr 2022