Entropy Mixing Networks: Enhancing Pseudo-Random Number Generators with Lightweight Dynamic Entropy Injection

TL;DR

The paper introduces Entropy Mixing Networks (EMN), a hybrid pseudo-random generator that improves randomness quality through dynamic entropy injection, outperforming standard generators in statistical tests despite higher computational costs.

Contribution

It proposes a novel hybrid generator with a comprehensive evaluation framework, demonstrating superior randomness quality over existing methods.

Findings

EMN achieves the highest Chi-squared p-value (0.9430)

EMN attains the highest entropy (7.9840)

EMN has the lowest predictability (-0.0286)

Abstract

Random number generation plays a vital role in cryptographic systems and computational applications, where uniformity, unpredictability, and robustness are essential. This paper presents the Entropy Mixing Network (EMN), a novel hybrid random number generator designed to enhance randomness quality by combining deterministic pseudo-random generation with periodic entropy injection. To evaluate its effectiveness, we propose a comprehensive assessment framework that integrates statistical tests, advanced metrics, and visual analyses, providing a holistic view of randomness quality, predictability, and computational efficiency. The results demonstrate that EMN outperforms Python's SystemRandom and MersenneTwister in critical metrics, achieving the highest Chi-squared p-value (0.9430), entropy (7.9840), and lowest predictability (-0.0286). These improvements come with a trade-off in computational performance, as EMN incurs a higher generation time (0.2602 seconds). Despite this, its superior randomness quality makes it particularly suitable for cryptographic applications where security is prioritized over speed.