Parameter Synthesis for Probabilistic Hyperproperties

20 pages•Published: May 27, 2020

Erika Abraham, Ezio Bartocci, Borzoo Bonakdarpour and Oyendrila Dobe

Abstract

In this paper, we study the parameter synthesis problem for probabilistic hyperproper- ties. A probabilistic hyperproperty stipulates quantitative dependencies among a set of executions. In particular, we solve the following problem: given a probabilistic hyperprop- erty ψ and discrete-time Markov chain D with parametric transition probabilities, compute regions of parameter configurations that instantiate D to satisfy ψ, and regions that lead to violation. We address this problem for a fragment of the temporal logic HyperPCTL that allows expressing quantitative reachability relation among a set of computation trees. We illustrate the application of our technique in the areas of differential privacy, probabilistic nonintereference, and probabilistic conformance.

Keyphrases: information flow security, probabilistic systems, synthesis

In: Elvira Albert and Laura Kovacs (editors). LPAR23. LPAR-23: 23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 73, pages 12-31.

Links:	https://easychair.org/publications/paper/BTZg
	https://doi.org/10.29007/37lf

BibTeX entry

@inproceedings{LPAR23:Parameter_Synthesis_Probabilistic_Hyperproperties,
  author    = {Erika Abraham and Ezio Bartocci and Borzoo Bonakdarpour and Oyendrila Dobe},
  title     = {Parameter Synthesis for Probabilistic Hyperproperties},
  booktitle = {LPAR23. LPAR-23: 23rd International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Elvira Albert and Laura Kovacs},
  series    = {EPiC Series in Computing},
  volume    = {73},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/BTZg},
  doi       = {10.29007/37lf},
  pages     = {12-31},
  year      = {2020}}

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