(2020) Probabilistic Hyperproperties of Markov Decision Processes.
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Abstract
Hyperproperties are properties that describe the correctness of a system as a relation between multiple executions. Hyperproperties generalize trace properties and include information-flow security requirements, like noninterference, as well as requirements like symmetry, partial observation, robustness, and fault tolerance. We initiate the study of the specification and verification of hyperproperties of Markov decision processes (MDPs). We introduce the temporal logic PHL (Probabilistic Hyper Logic), which extends classic probabilistic logics with quantification over schedulers and traces. PHL can express a wide range of hyperproperties for probabilistic systems, including both classical applications, such as probabilistic noninterference, and novel applications in areas such as robotics and planning. While the model checking problem for PHL is in general undecidable, we provide methods both for proving and for refuting formulas from a fragment of the logic. The fragment includes many probabilistic hyperproperties of interest.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
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| Divisions: | Bernd Finkbeiner (Reactive Systems Group, RSG) |
| Conference: | ATVA International Symposium on Automated Technology for Verification and Analysis |
| Depositing User: | Rayna Dimitrova |
| Date Deposited: | 07 Dec 2020 09:51 |
| Last Modified: | 07 Dec 2020 09:51 |
| Primary Research Area: | NRA2: Reliable Security Guarantees |
| URI: | https://publications.cispa.saarland/id/eprint/3320 |
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