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

2025-01-14

AI Summary

To address the insufficient randomness, high predictability, and limited computational efficiency of existing pseudorandom number generators (PRNGs) in cryptographic applications, this paper proposes the Entropy Mixing Network (EMN)—a lightweight, dynamic entropy-injection architecture that synergistically integrates deterministic PRNGs with periodic true entropy sources while minimizing computational overhead. We innovatively design a dynamic entropy mixing mechanism and establish a multidimensional evaluation framework comprising statistical testing (χ² test, p = 0.9430), Shannon entropy (7.984 bits/byte), and LSTM-based predictability modeling (prediction score: −0.0286). Experimental results demonstrate that EMN significantly outperforms SystemRandom and the Mersenne Twister, achieving superior randomness, robust unpredictability, and real-time performance—making it particularly suitable for high-assurance cryptographic scenarios.

Technology Category

• Machine Learning: Privacy
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Applications of cryptography
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Random Number Generator
• Randomness
• Cryptography

Innovation

• Entropy Mixing Network
• Random Number Generator
• Cryptography