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  • 标题:Brief Announcement: On Strong Observational Refinement and Forward Simulation
  • 本地全文:下载
  • 作者:Derrick, John ; Doherty, Simon ; Dongol, Brijesh
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2021
  • 卷号:209
  • DOI:10.4230/LIPIcs.DISC.2021.55
  • 语种:English
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要: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 finite traces only. 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 progress condition, and prove that this progressive forward simulation does imply strong observational refinement.
  • 关键词:Strong Observational Refinement;Hyperproperties;Forward Simulation
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