Neural Estimation of Information Leakage for Secure Communication System Design
Underestimating the leakage can compromise secrecy, while overestimating it may lead to inefficient system design. Therefore, a reliable leakage estimator is essential. Neural network-based estimators provide a data-driven way to estimate mutual information without requiring full knowledge of the channel or source distributions. In this work, we aim to scale the blocklength of a wiretap code such that the estimator can still feasibly operate. We propose an improved mutual information estimator based on the variational contrastive log-ration upper bound framework, tailored for both discrete and continuous variables. By using a mixture of Bernoulli experts parameterized by neural networks, the estimator is able to quantify information leakage in communication systems, which employ complex data processing like universal hash family. We further propose a method to utilize the proposed estimator to design the universal hash family for a wiretap code or secret key generation design. Simulation results show thatprior methods significantly underestimate the mutual information, particularly when using universal hash family for higher blocklengths ($n\gg$16). The proposed method can scale the blocklength up to 255, and we conjecture that the design can scale well to even higher blocklengths given adequate training data and model size. Additionally, we contend that our proposed estimator and adaptive hash design framework offer a practical approach for extending physical layer security considerations for wiretap channels into the finite blocklength regime.