Generalized Bisimulation Metrics

  • Konstantinos Chatzikokolakis
  • Daniel Gebler
  • Catuscia Palamidessi
  • Lili Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8704)


The bisimilarity pseudometric based on the Kantorovich lifting is one of the most popular metrics for probabilistic processes proposed in the literature. However, its application in verification is limited to linear properties. We propose a generalization of this metric which allows to deal with a wider class of properties, such as those used in security and privacy. More precisely, we propose a family of metrics, parametrized on a notion of distance which depends on the property we want to verify. Furthermore, we show that the members of this family still characterize bisimilarity in terms of their kernel, and provide a bound on the corresponding metrics on traces. Finally, we study the case of a metric corresponding to differential privacy. We show that in this case it is possible to have a dual form, easier to compute, and we prove that the typical constructs of process algebra are non-expansive with respect to this metrics, thus paving the way to a modular approach to verification.


Dual Form Parallel Composition Process Algebra Probabilistic Process Total Variation Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Konstantinos Chatzikokolakis
    • 1
    • 2
  • Daniel Gebler
    • 3
  • Catuscia Palamidessi
    • 4
    • 2
  • Lili Xu
    • 2
    • 5
  1. 1.CNRSFrance
  2. 2.LIX, Ecole PolytechniqueFrance
  3. 3.VU University AmsterdamNetherlands
  4. 4.INRIAFrance
  5. 5.Institute of SoftwareChinese Academy of ScienceChina

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