Probabilistic Opacity for Markov Decision Processes - Archive ouverte HAL
Article Dans Une Revue Information Processing Letters Année : 2015

Probabilistic Opacity for Markov Decision Processes

Béatrice Bérard
Krishnendu Chatterjee
  • Fonction : Auteur
Nathalie Sznajder

Résumé

Opacity is a generic security property, that has been defined on (non-probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an external observer, the value of interest for opacity is a measure of the set of runs disclosing the secret. We extend this definition to the richer framework of Markov decision processes, where non-deterministic choice is combined with probabilistic transitions, and we study related decidability problems with partial or complete observation hypotheses for the schedulers. We prove that all questions are decidable with complete observation and ω-regular secrets. With partial observation, we prove that all quantitative questions are undecidable but the question whether a system is almost surely non-opaque becomes decidable for a restricted class of ω-regular secrets, as well as for all ω-regular secrets under finite-memory schedulers.

Dates et versions

hal-01176435 , version 1 (15-07-2015)

Identifiants

Citer

Béatrice Bérard, Krishnendu Chatterjee, Nathalie Sznajder. Probabilistic Opacity for Markov Decision Processes. Information Processing Letters, 2015, 115 (1), pp.52-59. ⟨10.1016/j.ipl.2014.09.001⟩. ⟨hal-01176435⟩
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