Computing and estimating information leakage with a quantitative point-to-point information flow model

Information leakage occurs when a system exposes its secret information to an unauthorised entity. Information flow analysis is concerned with tracking flows of information through systems to determine whether they process information securely or leak information. We present a novel information flow model that permits an arbitrary amount of secret and publicly-observable information to occur at any point and in any order in a system. This is an improvement over previous models, which generally assume that systems process a single piece of secret information present before execution and produce a single piece of publicly-observable information upon termination. Our model precisely quantifies the information leakage from secret to publicly-observable values at user-defined points - hence, a "point-to-point" model - using the information-theoretic measures of mutual information and min-entropy leakage; it is ideal for analysing systems of low to moderate complexity. We also present a relaxed version of our information flow model that estimates, rather than computes, the measures of mutual information and min-entropy leakage via sampling of a system. We use statistical techniques to bound the accuracy of the estimates this model provides. We demonstrate how our relaxed model is more suitable for analysing complex systems by implementing it in a quantitative information flow analysis tool for Java programs