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Towards Quantifying Privacy in Process Mining

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Process Mining Workshops (ICPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 406))

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Abstract

Process mining employs event logs to provide insights into the actual processes. Event logs are recorded by information systems and contain valuable information helping organizations to improve their processes. However, these data also include highly sensitive private information which is a major concern when applying process mining. Therefore, privacy preservation in process mining is growing in importance, and new techniques are being introduced. The effectiveness of the proposed privacy preservation techniques needs to be evaluated. It is important to measure both sensitive data protection and data utility preservation. In this paper, we propose an approach to quantify the effectiveness of privacy preservation techniques. We introduce two measures for quantifying disclosure risks to evaluate the sensitive data protection aspect. Moreover, a measure is proposed to quantify data utility preservation for the main process mining activities. The proposed measures have been tested using various real-life event logs.

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Notes

  1. 1.

    https://github.com/m4jidRafiei/privacy_quantification.

  2. 2.

    Note that this \(\mathrm {L}\) is identical to the l introduced as the power (size) of background knowledge and should not be confused with \(L\) as the event log notation.

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Acknowledgment

Funded under the Excellence Strategy of the Federal Government and the Länder. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.

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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

Authors
  1. Majid Rafiei
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  2. Wil M. P. van der Aalst
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Correspondence to Majid Rafiei .

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Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Sander Leemans

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

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Rafiei, M., van der Aalst, W.M.P. (2021). Towards Quantifying Privacy in Process Mining. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_29

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72692-8

  • Online ISBN: 978-3-030-72693-5

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