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Lightweight Verification of Hyperproperties

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Automated Technology for Verification and Analysis (ATVA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14216))

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Abstract

Hyperproperties have been widely used to express system properties like noninterference, observational determinism, conformance, robustness, etc. However, the model checking problem for hyperproperties is challenging due to its inherent complexity of verifying properties across sets of traces and suffers from scalability issues. Previously, statistical approaches have proven effective in tackling the scalability of model checking for temporal logic. In this work, we have attempted to combine these two concepts to propose a tractable solution to model checking of hyperproperties expressed as HyperLTL on models involving nondeterminism. We have implemented our approach in PLASMA and experimented with a range of case studies to showcase its effectiveness.

This project was partially funded by the United States NSF SaTC Awards 2245114 and 2100989, NSF Award CCF-2133160, FWF-project ZK-35, FNRS PDR - T013721, and by the Vienna Science and Technology Fund (WWTF) [10.47379/ICT19018].

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Authors and Affiliations

  1. Michigan State University, East Lansing, USA

    Oyendrila Dobe & Borzoo Bonakdarpour

  2. Technische Universität Wien, Vienna, Austria

    Stefan Schupp & Ezio Bartocci

  3. UCLouvain, Ottignies-Louvain-la-Neuve, Belgium

    Axel Legay

  4. Duke University, Durham, USA

    Miroslav Pajic

  5. University of Florida, Gainesville, USA

    Yu Wang

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  1. Oyendrila Dobe
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  2. Stefan Schupp
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  3. Ezio Bartocci
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  4. Borzoo Bonakdarpour
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  5. Axel Legay
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  6. Miroslav Pajic
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  7. Yu Wang
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Correspondence to Borzoo Bonakdarpour .

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Editors and Affiliations

  1. Université Sorbonne Paris Nord, Villetaneuse, France

    Étienne André

  2. Singapore Management University, Singapore, Singapore

    Jun Sun

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Dobe, O. et al. (2023). Lightweight Verification of Hyperproperties. In: André, É., Sun, J. (eds) Automated Technology for Verification and Analysis. ATVA 2023. Lecture Notes in Computer Science, vol 14216. Springer, Cham. https://doi.org/10.1007/978-3-031-45332-8_1

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