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HyperProb: A Model Checker for Probabilistic Hyperproperties

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Formal Methods (FM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 13047))

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Abstract

We present HyperProb, a model checker to verify probabilistic hyperproperties on Markov Decision Processes (MDP). Our tool receives as input an MDP expressed as a PRISM model and a formula in Hyper Probabilistic Computational Tree Logic (HyperPCTL). By restricting the domain of scheduler quantification to memoryless non-probabilistic schedulers, our tool exploits an SMT-based encoding to model check probabilistic hyperproperties in HyperPCTL. Furthermore, when the property is satisfied, the tool can provide a witness that can be used for synthesizing a DTMC that conforms with the specification.

This work is sponsored in part by the United States NSF SaTC-1813388 grant.

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Notes

  1. 1.

    https://www.prismmodelchecker.org/.

References

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Author information

Authors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Oyendrila Dobe & Borzoo Bonakdarpour

  2. RWTH-Aachen, Aachen, Germany

    Erika Ábrahám

  3. Technische Universität Wien, Vienna, Austria

    Ezio Bartocci

Authors
  1. Oyendrila Dobe
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  2. Erika Ábrahám
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  3. Ezio Bartocci
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  4. Borzoo Bonakdarpour
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Corresponding author

Correspondence to Borzoo Bonakdarpour .

Editor information

Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Marieke Huisman

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Corina Păsăreanu

  3. Institute of Software, Chinese Academy of Sciences, Beijing, China

    Naijun Zhan

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Dobe, O., Ábrahám, E., Bartocci, E., Bonakdarpour, B. (2021). HyperProb: A Model Checker for Probabilistic Hyperproperties. In: Huisman, M., Păsăreanu, C., Zhan, N. (eds) Formal Methods. FM 2021. Lecture Notes in Computer Science(), vol 13047. Springer, Cham. https://doi.org/10.1007/978-3-030-90870-6_35

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  • DOI: https://doi.org/10.1007/978-3-030-90870-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90869-0

  • Online ISBN: 978-3-030-90870-6

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