Revisiting Prediction-Based Min-Entropy Estimation: Toward Interpretability, Reliability, and Applicability

Abstract

Prediction-based min-entropy estimation methods, also known as predictors, are essential tools for assessing the security of entropy sources. As recommended in NIST SP 800-90B (90B), these methods estimate min-entropy by forecasting the outputs of entropy sources. Owing to their computational efficiency, considerable research has focused on enhancing the accuracy of predictors, including approaches based on deep neural networks (DNNs). However, concerns remain about their interpretability, reliability, and applicability, particularly for DNN-based predictors. In this paper, we first identify key deficiencies in existing prediction-based methods, including those in 90B and DNN-based predictors, which lead to unreliable estimates and poor adaptability across diverse entropy sources. To improve reliability, we model the predictor output distribution and revise the local predictability metric to produce more stable estimates with associated confidence levels. To enhance the interpretability of DNN-based predictors in entropy estimation, we provide the first theoretical analysis linking neural network optimization objectives to min-entropy, clarifying the suitability and learnability of different architectures. We further reveal the inapplicability of existing methods under time-varying sources and propose a new estimation framework that combines online learning, change detection, and Bayesian optimization for dynamic model updates. The experimental results demonstrate that our methods surpass existing approaches in terms of reliability and applicability, especially when dealing with time-varying sources.