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Mining Roles from Event Logs While Preserving Privacy

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Business Process Management Workshops (BPM 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 362))

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Abstract

Process mining aims to provide insights into the actual processes based on event data. These data are widely available and often contain private information about individuals. On the one hand, knowing which individuals (known as resources) performed specific activities can be used for resource behavior analyses like role mining and is indispensable for bottleneck analysis. On the other hand, event data with resource information are highly sensitive. Process mining should reveal insights in the form of annotated models, but should not reveal sensitive information about individuals. In this paper, we show that the problem cannot be solved by naïve approaches like encrypting data, and an anonymized person can still be identified based on a few well-chosen events. We, therefore, introduce a decomposition method and a collection of techniques that preserve the privacy of the individuals, yet, at the same time, roles can be discovered and used for further bottleneck analyses without revealing sensitive information about individuals. To evaluate our approach, we have implemented an interactive environment and applied our approach to several real-life and artificial event logs.

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Notes

  1. 1.

    A deterministic cryptosystem produces the same ciphertext for a given plaintext and key.

  2. 2.

    H is a one-way hash function, here we use SHA-256.

  3. 3.

    Note that the substitution sets should not be revealed.

  4. 4.

    We consider a dummy resource in case there is an activity without resource.

  5. 5.

    https://github.com/m4jidRafiei/privacyAware-roleMining.

References

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Author information

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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Corresponding author

Correspondence to Majid Rafiei .

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Di Francescomarino

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco Dijkman

  3. University of Vienna, Vienna, Austria

    Uwe Zdun

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Rafiei, M., van der Aalst, W.M.P. (2019). Mining Roles from Event Logs While Preserving Privacy. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_54

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  • DOI: https://doi.org/10.1007/978-3-030-37453-2_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37452-5

  • Online ISBN: 978-3-030-37453-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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