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.
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The author has received funding within the BPR4GDPR project from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 787149.
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Mozafari Mehr, A.S., de Carvalho, R.M., van Dongen, B. (2021). Detecting Privacy, Data and Control-Flow Deviations in Business Processes. In: Nurcan, S., Korthaus, A. (eds) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-79108-7_10
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DOI: https://doi.org/10.1007/978-3-030-79108-7_10
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