A Probabilistic Small Model Theorem to Assess Confidentiality of Dispersed Cloud Storage

Baldi, Marco; Bartocci, Ezio; Chiaraluce, Franco; Cucchiarelli, Alessandro; Senigagliesi, Linda; Spalazzi, Luca; Spegni, Francesco

doi:10.1007/978-3-319-66335-7_8

Cited by 3 publications

(2 citation statements)

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“…An MDP is a state-based model representing a probabilistic process that satisfies the Markov property (memorylessness), where for each state it is possible to choose nondeterministically some action-labeled transitions governing the probability distribution to end up in the next state. MDPs are employed in several applications including the analysis of queueing systems [40], bird flocking [30], confidentiality [5] and robotics [32].…”

Section: Introductionmentioning

confidence: 99%

“…Our contribution. We analyze pMDPs with reachability objectives 5 without assuming that parameter values are accessible directly during execution. Specifically, we find parameter-independent strategies that are expectation ε-optimal [1], for ε ≥ 0, i.e., optimize the expected reachability probability given a probability distribution over the parameters.…”

Section: Introductionmentioning

confidence: 99%

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Parameter-Independent Strategies for pMDPs via POMDPs

Arming

Bartocci

Chatterjee

et al. 2018

Quantitative Evaluation of Systems

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Markov Decision Processes (MDPs) are a popular class of models suitable for solving control decision problems in probabilistic reactive systems. We consider parametric MDPs (pMDPs) that include parameters in some of the transition probabilities to account for stochastic uncertainties of the environment such as noise or input disturbances. We study pMDPs with reachability objectives where the parameter values are unknown and impossible to measure directly during execution, but there is a probability distribution known over the parameter values. We study for the first time computing parameter-independent strategies that are expectation optimal, i.e., optimize the expected reachability probability under the probability distribution over the parameters. We present an encoding of our problem to partially observable MDPs (POMDPs), i.e., a reduction of our problem to computing optimal strategies in POMDPs. We evaluate our method experimentally on several benchmarks: a motivating (repeated) learner model; a series of benchmarks of varying configurations of a robot moving on a grid; and a consensus protocol.

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