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Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement

Authors Brijesh Dongol , Gerhard Schellhorn, Heike Wehrheim



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Author Details

Brijesh Dongol
  • University of Surrey, UK
Gerhard Schellhorn
  • Universität Augsburg, Germany
Heike Wehrheim
  • Universität Oldenburg, Germany

Acknowledgements

We thank John Derrick, Simon Doherty, Constantin Enea and our anonymous CONCUR reviewers for their comments on earlier versions of this paper.

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Brijesh Dongol, Gerhard Schellhorn, and Heike Wehrheim. Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement. In 33rd International Conference on Concurrency Theory (CONCUR 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 243, pp. 31:1-31:23, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.CONCUR.2022.31

Abstract

Hyperproperties are correctness conditions for labelled transition systems that are more expressive than traditional trace properties, with particular relevance to security. Recently, Attiya and Enea studied a notion of strong observational refinement that preserves all hyperproperties. They analyse the correspondence between forward simulation and strong observational refinement in a setting with only finite traces. We study this correspondence in a setting with both finite and infinite traces. In particular, we show that forward simulation does not preserve hyperliveness properties in this setting. We extend the forward simulation proof obligation with a (weak) progress condition, and prove that this weak progressive forward simulation is equivalent to strong observational refinement.

Subject Classification

ACM Subject Classification
  • Theory of computation → Semantics and reasoning
  • Theory of computation → Concurrency
  • Security and privacy → Formal methods and theory of security
Keywords
  • Strong Observational Refinement
  • Hyperproperties
  • Forward Simulation
  • Weak Progressiveness

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