Machine Learning Predictors for Min-Entropy Estimation

arXiv:2406.19983v1 Announce Type: cross
Abstract: This study investigates the application of machine learning predictors for min-entropy estimation in Random Number Generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors, and indeed any predictor that leverages sequence correlations, primarily estimate average min-entropy, a metric not extensively studied in this context. We explore the relationship between average min-entropy and the traditional min-entropy, focusing on their dependence on the number …

application applications arxiv assessment cryptographic cs.cr cs.it cs.lg cybersecurity entropy indeed key machine machine learning math.it random random number research study