Abstract
Probabilistic hyperproperties describe system properties involving probability measures over multiple runs and have numerous applications in information-flow security. However, the poor scalability of existing model checking algorithms for probabilistic hyperproperties limits their use to small models. In this paper, we propose a model checking algorithm to verify discrete-time Markov chains (DTMC) against HyperPCTL formulas by integrating numerical solution techniques. Our algorithm is based on a probabilistic decomposition of the self-composed DTMC into variants of the underlying DTMC. Experimentally, we show that our algorithm significantly outperforms both a symbolic approach and the original approach based on brute-force self-composition.
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Zaman, E., Ciardo, G., Ábrahám, E., Bonakdarpour, B. (2022). HyperPCTL Model Checking by Probabilistic Decomposition. In: ter Beek, M.H., Monahan, R. (eds) Integrated Formal Methods. IFM 2022. Lecture Notes in Computer Science, vol 13274. Springer, Cham. https://doi.org/10.1007/978-3-031-07727-2_12
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