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

Authors:
Bao Trung Chu
Kenji Hashimoto
Hiroyuki Seki

Keywords: Dynamic leakage; Composition; Quantitative Infor- mation Flow; BDD; d-DNNF.

Abstract:
Quantitative Information Flow (QIF), as summed up by Smith (2019), is traditionally defined as the expected value of in- formation leakage over all feasible program runs. The traditional QIF fails to identify vulnerable programs where only a limited number of runs leak large amount of information. As discussed in Bielova (2016), a good notion for dynamic leakage and an efficient way of computing the leakage are needed. To address this problem, the authors have already proposed two notions for dynamic leakage and a method of quantifying dynamic leakage based on model counting. Inspired by the work of Kawamoto et al. (2017), this paper proposes two efficient methods for computing dynamic leakage, a compositional method along with the sequential structure of a program and a parallel computation based on the disjoint value domain decomposition. For the former, we investigate both exact and approximated calculations. For implementation, we utilize Binary Decision Diagrams (BDDs) and deterministic Decomposable Negation Normal Forms (d-DNNFs) to represent Boolean formulas in model counting. Finally, we show experimental results on several examples.

Pages: 1 to 8

Copyright: Copyright (c) IARIA, 2019

Publication date: October 27, 2019

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-746-7

Location: Nice, France

Dates: from October 27, 2019 to October 31, 2019