Significant Diagnostic Counterexamples in Probabilistic Model Checking

Andrés, Miguel; D’Argenio, Pedro R.; Rossum, Peter van

doi:10.1007/978-3-642-01702-5_15

Cited by 42 publications

(43 citation statements)

References 16 publications

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“…In [2], paths that only differ in the way they traverse strongly connected components (SCC's) are grouped together. Note that in our case, such paths have the same secret and observable trace since secret and observable actions cannot occur on cycles.…”

Section: Definition 54 the Function Val : R(σ) → R Evaluates Regulamentioning

confidence: 99%

“…Note that in our case, such paths have the same secret and observable trace since secret and observable actions cannot occur on cycles. Following [2], we first abstract away the SCC's, leaving only probabilistic transitions that go immediately from an entry point of the SCC to an exit point (called input and output states in [2]). This abstraction happens in such a way that the observable behaviour of the automaton does not change.…”

Section: Definition 54 the Function Val : R(σ) → R Evaluates Regulamentioning

confidence: 99%

“…This abstraction happens in such a way that the observable behaviour of the automaton does not change. Again, instead of going into technical details (which also involves translating the work [2] from Markov chains to fully probabilistic automata), we show an example.…”

Section: Definition 54 the Function Val : R(σ) → R Evaluates Regulamentioning

confidence: 99%

“…Indeed, this is the aim of the counterexamples generation techniques previously presented. For further details on how to debug sets of paths and why these techniques meet that purpose we refer to [1,8,2]. Example 5.8.…”

Section: )) In Fact the Multiplicative Leakage Is Given Dividing V(mentioning

confidence: 99%

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Computing the Leakage of Information-Hiding Systems

Andrés¹,

Palamidessi

Rossum³

et al. 2010

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We address the problem of computing the information leakage of a system in an efficient way. We propose two methods: one based on reducing the problem to reachability, and the other based on techniques from quantitative counterexample generation. The second approach can be used either for exact or approximate computation, and provides feedback for debugging. These methods can be applied also in the case in which the input distribution is unknown. We then consider the interactive case and we point out that the definition of associated channel proposed in literature is not sound. We show however that the leakage can still be defined consistently, and that our methods extend smoothly.

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Section: Definition 54 the Function Val : R(σ) → R Evaluates Regulamentioning

confidence: 99%

Section: Definition 54 the Function Val : R(σ) → R Evaluates Regulamentioning

confidence: 99%

Section: Definition 54 the Function Val : R(σ) → R Evaluates Regulamentioning

confidence: 99%

Section: )) In Fact the Multiplicative Leakage Is Given Dividing V(mentioning

confidence: 99%

See 2 more Smart Citations

Computing the Leakage of Information-Hiding Systems

Andrés¹,

Palamidessi

Rossum³

et al. 2010

Tools and Algorithms for the Construction and Analysis of Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Probabilistic counterexamples [32,1,3] provide diagnostic information to the user of a probabilistic model checker if a temporal logic query is found to be false. Typically, this takes the form of a set of violating system executions, whose combined probability exceeds some desired threshold.…”

Section: Research Directionsmentioning

confidence: 99%

Advances in Probabilistic Model Checking

Katoen

2010

Lecture Notes in Computer Science

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Abstract. Probabilistic model checking is an automated verification method that aims to establish the correctness of probabilistic systems. Probability may arise, for example, due to failures of unreliable components, communication across lossy media, or through the use of randomisation in distributed protocols. Probabilistic model checking enables a range of exhaustive, quantitative analyses of properties such as "the probability of a message being delivered within 5ms is at least 0.89".In the last ten years, probabilistic model checking has been successfully applied to numerous real-world case studies, and is now a highly active field of research. This tutorial gives an introduction to probabilistic model checking, as well as presenting material on selected recent advances. The first half of the tutorial concerns two classical probabilistic models, discrete-time Markov chains and Markov decision processes, explaining the underlying theory and model checking algorithms for the temporal logic PCTL. The second half discusses two advanced topics: quantitative abstraction refinement and model checking for probabilistic timed automata. We also briefly summarise the functionality of the probabilistic model checker PRISM, the leading tool in the area.

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Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

Funke

Jantsch

Baier

2020

Lecture Notes in Computer Science

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This paper introduces Farkas certificates for lower and upper bounds on minimal and maximal reachability probabilities in Markov decision processes (MDP), which we derive using an MDP-variant of Farkas' Lemma. The set of all such certificates is shown to form a polytope whose points correspond to witnessing subsystems of the model and the property. This allows translating the problem of finding minimal witnesses to the problem of finding vertices with a maximal number of zeros. While computing such vertices is computationally hard in general, we derive new heuristics from our formulations that exhibit competitive performance compared to state-of-the-art techniques and apply to more situations. As an argument that asymptotically better algorithms cannot be hoped for, we show that the decision version of finding minimal witnesses is NP-complete even for acyclic Markov chains.

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Significant Diagnostic Counterexamples in Probabilistic Model Checking

Cited by 42 publications

References 16 publications

Computing the Leakage of Information-Hiding Systems

Computing the Leakage of Information-Hiding Systems

Advances in Probabilistic Model Checking

Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

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