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

3 Citations (Scopus)

25 Downloads (Pure)

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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CAiSE_2021-AuthorVersion

Cite this

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",

author = "{Mozafari Mehr}, Azadeh and {Medeiros de Carvalho}, Renata and {van Dongen}, {Boudewijn F.}",

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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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