Information Leakage Detection through Approximate Bayes-optimal Prediction
This addresses security concerns in data-driven systems by providing a more accurate detection method for information leaks, though it builds incrementally on existing approaches.
The paper tackles the information leakage detection problem by establishing a theoretical framework that uses statistical learning theory and information theory to quantify and detect leaks accurately, demonstrating superior performance to state-of-the-art baselines on synthetic and real-world datasets.
In today's data-driven world, the proliferation of publicly available information raises security concerns due to the information leakage (IL) problem. IL involves unintentionally exposing sensitive information to unauthorized parties via observable system information. Conventional statistical approaches rely on estimating mutual information (MI) between observable and secret information for detecting ILs, face challenges of the curse of dimensionality, convergence, computational complexity, and MI misestimation. Though effective, emerging supervised machine learning based approaches to detect ILs are limited to binary system sensitive information and lack a comprehensive framework. To address these limitations, we establish a theoretical framework using statistical learning theory and information theory to quantify and detect IL accurately. Using automated machine learning, we demonstrate that MI can be accurately estimated by approximating the typically unknown Bayes predictor's log-loss and accuracy. Based on this, we show how MI can effectively be estimated to detect ILs. Our method performs superior to state-of-the-art baselines in an empirical study considering synthetic and real-world OpenSSL TLS server datasets.