ABSTRACT. In this paper we describe symbolic side-channel analysis techniques for detecting and quantifying information leakage, given in terms of Shannon and Min Entropy. Measuring the precise leakage is challenging due to the randomness and noise often present in program executions and side-channel observations. We account for this noise by introducing additional (symbolic) program inputs which are interpreted {\em probabilistically}, using symbolic execution with {\em parameterized} model counting. We also explore an approximate sampling approach for increased scalability. In contrast to typical Monte Carlo techniques, our approach works by sampling symbolic paths, representing multiple concrete paths, and uses pruning to accelerate computation and guarantee convergence to the optimal results. The key novelty of our approach is to provide bounds on the leakage that are provably under- and over-approximating the real leakage. We implemented the techniques in the Symbolic PathFinder tool and we demonstrate them on Java programs.