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

Azadeh Mozafari Mehr; Renata Medeiros de Carvalho; Boudewijn F. van Dongen

doi:https://doi.org/10.1007/978-3-030-79108-7_10

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

Azadeh Mozafari Mehr, Renata Medeiros de Carvalho, Boudewijn F. van Dongen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Intelligent Information Systems - CAiSE Forum 2021, Proceedings
Editors	Selmin Nurcan, Axel Korthaus
Publisher	Springer
Chapter	10
Pages	82-91
Number of pages	10
ISBN (Electronic)	978-3-030-79108-7
ISBN (Print)	978-3-030-79107-0
DOIs	https://doi.org/10.1007/978-3-030-79108-7_10
Publication status	Published - 15 Jun 2021

Publication series

Name	Lecture Notes in Business Information Processing
Volume	424 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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Mozafari Mehr, A., Medeiros de Carvalho, R., & van Dongen, B. F. (2021). Detecting Privacy, Data and Control-Flow Deviations in Business Processes. In S. Nurcan, & A. Korthaus (Eds.), Intelligent Information Systems - CAiSE Forum 2021, Proceedings (pp. 82-91). (Lecture Notes in Business Information Processing; Vol. 424 LNBIP). Springer. https://doi.org/10.1007/978-3-030-79108-7_10

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keywords = "Conformance checking, Data privacy, Multi-layer alignment, Multi-perspective analysis, Process mining",

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Detecting Privacy, Data and Control-Flow Deviations in Business Processes. / Mozafari Mehr, Azadeh ; Medeiros de Carvalho, Renata ; van Dongen, Boudewijn F.

Intelligent Information Systems - CAiSE Forum 2021, Proceedings. ed. / Selmin Nurcan; Axel Korthaus. Springer, 2021. p. 82-91 (Lecture Notes in Business Information Processing; Vol. 424 LNBIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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

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