Research Article
A Unified NIST SP 800-90B Validation Framework for CMOS True Random Number Generators and Quantum Random Number Generators
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 91 |
| Published: March 2026 |
| Authors: Che-Ping Lin |
10.5120/ijca2026926629
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Che-Ping Lin . A Unified NIST SP 800-90B Validation Framework for CMOS True Random Number Generators and Quantum Random Number Generators. International Journal of Computer Applications. 187, 91 (March 2026), 34-40. DOI=10.5120/ijca2026926629
@article{ 10.5120/ijca2026926629,
author = { Che-Ping Lin },
title = { A Unified NIST SP 800-90B Validation Framework for CMOS True Random Number Generators and Quantum Random Number Generators },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 91 },
pages = { 34-40 },
doi = { 10.5120/ijca2026926629 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Che-Ping Lin
%T A Unified NIST SP 800-90B Validation Framework for CMOS True Random Number Generators and Quantum Random Number Generators%T
%J International Journal of Computer Applications
%V 187
%N 91
%P 34-40
%R 10.5120/ijca2026926629
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Random number generators (RNGs) are foundational to modern cryptographic systems. While quantum random number generators (QRNGs) leverage inherently stochastic quantum measurements, practical deployments still exhibit implementation artifacts (e.g., detector dead time, afterpulsing, drift) that can introduce bias, correlation, and non-stationarity. This paper presents a unified validation framework for both CMOS true random number generators (TRNGs) and QRNGs under NIST SP 800-90B entropy source validation. Rather than simulating quantum physics, we model observable raw-output behaviors of practical implementations and evaluate them using a consistent pipeline: raw capture constraints, IID screening, conservative min-entropy bounding, and online health testing (repetition count test and adaptive proportion test). Using synthetic sources that emulate typical CMOS and QRNG failure modes, we demonstrate how similar artifacts drive the IID versus non-IID decision and tighten entropy bounds, motivating a vendor-agnostic, reproducible validation methodology.
References
- NIST, “Recommendation for the Entropy Sources Used for Random Bit Generation,” NIST SP 800-90B, 2018.
- NIST, “Recommendation for Random Bit Generator (RBG) Constructions,” NIST SP 800-90C, 2022.
- NIST CMVP, “Entropy Certificate #E63 Public Use: IDQ Quantis IID QRNG,” 2023.
- NIST CMVP, “Entropy Certificate #E246 Public Use: Qrypt Atlas Quantum Random Number Generator (QRNG) PCIe Card,” 2025.
- NIST, “SP 800-90B Entropy Assessment Tool (reference implementation),” GitHub repository: usnistgov/SP800-90B_EntropyAssessment, accessed 2026-02-19.
- ID Quantique, “Quantis QRNG Chip receives NIST Entropy Source Validation,” 2023.
- M. Layat-Kevin et al., “Observations on NIST SP 800-90B entropy estimators,” International Journal of Information Security, 2025.
- C.-P. Lin, “Continuous Entropy Monitoring of TRNGs via a CNN Autoencoder with Residual-Based Diagnostics,” Zenodo Preprint, 2026. DOI: 10.5281/zenodo.18338590.
- C. Caratozzolo, V. Rossi, K. Witek, A. Trombetta, M. Baszczyk, P. Dorosz, W. Kucewicz, and M. Caccia, “Entropy measurement and online quality control of bit streams by a true random bit generator,” Frontiers in Computer Science, vol. 7, art. 1642566, Oct. 2025. doi: 10.3389/fcomp.2025.1642566.
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