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International Symposium on Data-Driven Process Discovery and Analysis

International Symposium on Data-Driven Process Discovery and Analysis

SIMPDA 2018, SIMPDA 2019: Data-Driven Process Discovery and Analysis pp 101–123Cite as

Supporting Confidentiality in Process Mining Using Abstraction and Encryption

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 379)

Abstract

Process mining aims to bridge the gap between data science and process science by providing a variety of powerful data-driven analyses techniques on the basis of event data. These techniques encompass automatically discovering process models, detecting and predicting bottlenecks, and finding process deviations. In process mining, event data containing the full breadth of resource information allows for performance analysis and discovering social networks. On the other hand, event data are often highly sensitive, and when the data contain private information, privacy issues arise. Surprisingly, there has currently been little research toward security methods and encryption techniques for process mining. Therefore, in this paper, using abstraction, we propose an approach that allows us to hide confidential information in a controlled manner while ensuring that the desired process mining results can still be obtained. We show how our approach can support confidentiality while discovering control-flow and social networks. A connector method is applied as a technique for storing associations between events securely. We evaluate our approach by applying it on real-life event logs.

Keywords

  • Responsible process mining
  • Confidentiality
  • Process discovery
  • Directly follows graph
  • Social network analysis

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  • DOI: 10.1007/978-3-030-46633-6_6
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Acknowledgment

We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research interactions.

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Authors and Affiliations

  1. Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

    Majid Rafiei & Wil M. P. van der Aalst

  2. Yale University, New Haven, USA

    Leopold von Waldthausen

Authors
  1. Majid Rafiei
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  2. Leopold von Waldthausen
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  3. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Majid Rafiei .

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Editors and Affiliations

  1. Università degli Studi di Milano, Milan, Italy

    Prof. Paolo Ceravolo

  2. University of Twente, Enschede, The Netherlands

    Maurice van Keulen

  3. University of Seville, Seville, Spain

    María Teresa Gómez-López

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Rafiei, M., von Waldthausen, L., van der Aalst, W.M.P. (2020). Supporting Confidentiality in Process Mining Using Abstraction and Encryption. In: Ceravolo, P., van Keulen, M., Gómez-López, M. (eds) Data-Driven Process Discovery and Analysis. SIMPDA SIMPDA 2018 2019. Lecture Notes in Business Information Processing, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-46633-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-46633-6_6

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