Obtaining Information Leakage Bounds via Approximate Model Counting

Information leaks are a significant problem in modern software systems. In recent years, information theoretic concepts, such as Shannon entropy, have been applied to quantifying information leaks in programs. One recent approach is to use symbolic execution together with model counting constraints solvers in order to quantify information leakage. There are at least two reasons for unsoundness in quantifying information leakage using this approach: 1) Symbolic execution may not be able to explore all execution paths, 2) Model counting constraints solvers may not be able to provide an exact count. We present a sound symbolic quantitative information flow analysis that bounds the information leakage both for the cases where the program behavior is not fully explored and the model counting constraint solver is unable to provide a precise model count but provides an upper and a lower bound. We implemented our approach as an extension to KLEE for computing sound bounds for information leakage in C programs.