Detecting Privacy, Data and Control-Flow Deviations in Business Processes

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Abstract

Existing access control mechanisms are not sufficient for data protection. They are only preventive and cannot guarantee that data is accessed for the intended purpose. This paper proposes a novel approach for multi-perspective conformance checking which considers the control-flow, data and privacy perspectives of a business process simultaneously to find the context in which data is processed. In addition to detecting deviations in each perspective, the approach is able to detect hidden deviations where non-conformity relates to either a combination of two or all three aspects of a business process. The approach has been implemented in the open source ProM framework and was evaluated through controlled experiments using synthetic logs of a simulated real-life process.

Original languageEnglish
Title of host publicationIntelligent Information Systems - CAiSE Forum 2021, Proceedings
EditorsSelmin Nurcan, Axel Korthaus
PublisherSpringer
Chapter10
Pages82-91
Number of pages10
ISBN (Electronic)978-3-030-79108-7
ISBN (Print)978-3-030-79107-0
DOIs
Publication statusPublished - 15 Jun 2021

Publication series

NameLecture Notes in Business Information Processing
Volume424 LNBIP
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Keywords

  • Conformance checking
  • Data privacy
  • Multi-layer alignment
  • Multi-perspective analysis
  • Process mining

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