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Quantitative information flow (QIF) is traditionally defined as the expectedvalue of information leakage over all feasible program runs and it fails toidentify vulnerable programs where only limited number of runs leak largeamount of information. As discussed in Bielova (2016), a good notion fordynamic leakage and an efficient way of computing the leakage are needed. Toaddress this problem, the authors have already proposed two notions for dynamicleakage and a method of quantifying dynamic leakage based on model counting.Inspired by the work of Kawamoto et. al. (2017), this paper proposes twoefficient methods for computing dynamic leakage, a compositional method alongwith the sequential structure of a program and a parallel computation based onthe value domain decomposition. For the former, we also investigate both exactand approximated calculations. From the perspective of implementation, weutilize binary decision diagrams (BDDs) and deterministic decomposable negationnormal forms (d-DNNFs) to represent Boolean formulas in model counting.Finally, we show experimental results on several examples.

On the Compositionality of Dynamic Leakage and Its Application to the Quantification Problem