ArticleEntropy (Basel, Switzerland)2024
Statistical Testing of Random Number Generators and Their Improvement Using Randomness Extraction.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Medical Image Encryption via CSPRNG-HKDF Key Scheduling, Fractional-Order Modified Chen Hyperchaos, and Dynamic DNA-ARX Diffusion.Sensors (Basel, Switzerland) · 2026Article
- A Transparent, Simple, Audit-Oriented Python-Based Binary Allocation Prototype for Clinical Research.Cureus · 2026Article
- Generating borderline test samples for randomness testers via intelligent optimization and evolutionary algorithms.Scientific reports · 2026Article
- Raw QPP-RNG randomness via system jitter across platforms: a NIST SP 800-90B evaluation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. While statistical tests cannot definitively guarantee an RNG's output quality, they are a powerful verification tool and the only universally applicable testing method. In this work, we design, implement, and present various post-processing methods, using randomness extractors, to improve the RNG output quality and compare them through statistical testing. We begin by performing intensive tests on three RNGs-the 32-bit linear feedback shift register (LFSR), Intel's 'RDSEED,' and IDQuantique's 'Quantis'-and compare their performance. Next, we apply the different post-processing methods to each RNG and conduct further intensive testing on the processed output. To facilitate this, we introduce a comprehensive statistical testing environment, based on existing test suites, that can be parametrised for lightweight (fast) to intensive testing.
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