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Privacy-Preserving Continuous Event Data Publishing

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Business Process Management Forum (BPM 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 427))

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Abstract

Process mining enables organizations to discover and analyze their actual processes using event data. Event data can be extracted from any information system supporting operational processes, e.g., SAP. Whereas the data inside such systems is protected using access control mechanisms, the extracted event data contain sensitive information that needs to be protected. This creates a new risk and a possible inhibitor for applying process mining. Therefore, privacy issues in process mining become increasingly important. Several privacy preservation techniques have been introduced to mitigate possible attacks against static event data published only once. However, to keep the process mining results up-to-date, event data need to be published continuously. For example, a new log is created at the end of each week. In this paper, we elaborate on the attacks which can be launched against continuously publishing anonymized event data by comparing different releases, so-called correspondence attacks. Particularly, we focus on group-based privacy preservation techniques and show that provided privacy requirements can be degraded exploiting correspondence attacks. We apply the continuous event data publishing scenario to existing real-life event logs and report the anonymity indicators before and after launching the attacks.

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Notes

  1. 1.

    http://data.europa.eu/eli/reg/2016/679/oj.

  2. 2.

    https://github.com/m4jidRafiei/TLKC-Privacy-Ext.

  3. 3.

    https://github.com/m4jidRafiei/PP_CEDP.

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Acknowledgment

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

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Authors and Affiliations

  1. 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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Corresponding author

Correspondence to Majid Rafiei .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Carlton, VIC, Australia

    Artem Polyvyanyy

  2. NICTA Queensland Res Lab, Queensland University of Technology, Brisbane, QLD, Australia

    Moe Thandar Wynn

  3. EB24, Ghent University, Ghent, Belgium

    Amy Van Looy

  4. University of Ulm, Ulm, Germany

    Manfred Reichert

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Rafiei, M., van der Aalst, W.M.P. (2021). Privacy-Preserving Continuous Event Data Publishing. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management Forum. BPM 2021. Lecture Notes in Business Information Processing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-030-85440-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-85440-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85439-3

  • Online ISBN: 978-3-030-85440-9

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