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Privacy-Preserving Continuous Event Data Publishing

  • Conference paper
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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.

References

  1. van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. Aalst, W.M.P.: Responsible data science: using event data in a “people friendly’’ manner. In: Hammoudi, S., Maciaszek, L.A., Missikoff, M.M., Camp, O., Cordeiro, J. (eds.) ICEIS 2016. LNBIP, vol. 291, pp. 3–28. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62386-3_1

    Chapter  Google Scholar 

  3. Elkoumy, G., Fahrenkrog-Petersen, S.A., Dumas, M., Laud, P., Pankova, A., Weidlich, M.: Secure multi-party computation for inter-organizational process mining. In: Nurcan, S., Reinhartz-Berger, I., Soffer, P., Zdravkovic, J. (eds.) BPMDS/EMMSAD -2020. LNBIP, vol. 387, pp. 166–181. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49418-6_11

    Chapter  Google Scholar 

  4. Elkoumy, G., et al.: Privacy and confidentiality in process mining - threats and research challenges. CoRR abs/2106.00388 (2021). https://arxiv.org/abs/2106.00388

  5. Fahrenkrog-Petersen, S.A., van der Aa, H., Weidlich, M.: PRETSA: event log sanitization for privacy-aware process discovery. In: International Conference on Process Mining, ICPM 2019, Aachen, Germany (2019)

    Google Scholar 

  6. Fahrenkrog-Petersen, S.A., van der Aa, H., Weidlich, M.: PRIPEL: privacy-preserving event log publishing including contextual information. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 111–128. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_7

    Chapter  Google Scholar 

  7. Fung, B.C.M., Wang, K., Fu, A.W., Pei, J.: Anonymity for continuous data publishing. In: 11th International Conference on Extending Database Technology. ACM International Conference Proceeding Series, vol. 261, pp. 264–275 (2008)

    Google Scholar 

  8. Fung, B.C., Wang, K., Fu, A.W.C., Philip, S.Y.: Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques. Chapman and Hall/CRC, London (2010)

    Book  Google Scholar 

  9. Gehrke, J.: Models and methods for privacy-preserving data analysis and publishing. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE, p. 105. IEEE Computer Society (2006)

    Google Scholar 

  10. Mannhardt, F.: Sepsis Cases-Event Log. Eindhoven University of Technology (2016)

    Google Scholar 

  11. Mannhardt, F., Koschmider, A., Baracaldo, N., Weidlich, M., Michael, J.: Privacy-preserving process mining - differential privacy for event logs. Bus. Inf. Syst. Eng. 61(5), 595–614 (2019)

    Article  Google Scholar 

  12. Michael, J., Koschmide, A., Mannhardt, F., Baracaldo, N., Rumpe, B.: User-centered and privacy-driven process mining system design for IoT. In: Cappiello, C., Ruiz, M. (eds.) CAiSE 2019CAiSE 2019. LNBIP, vol. 350, pp. 194–206. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21297-1_17

    Chapter  Google Scholar 

  13. Pika, A., Wynn, M.T., Budiono, S., ter Hofstede, A.H., van der Aalst, W.M.P., Reijers, H.A.: Privacy-preserving process mining in healthcare. Int. J. Environ. Res. Public Health 17(5), 1612 (2020)

    Article  Google Scholar 

  14. Rafiei, M., van der Aalst, W.M.P.: Mining roles from event logs while preserving privacy. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 676–689. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_54

    Chapter  Google Scholar 

  15. Rafiei, M., van der Aalst, W.M.P.: Privacy-preserving data publishing in process mining. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 122–138. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58638-6_8

    Chapter  Google Scholar 

  16. Rafiei, M., van der Aalst, W.M.P.: Towards quantifying privacy in process mining. In: Leemans, S., Leopold, H. (eds.) ICPM 2020. LNBIP, vol. 406, pp. 385–397. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72693-5_29

    Chapter  Google Scholar 

  17. Rafiei, M., van der Aalst, W.M.P.: Group-based privacy preservation techniques for process mining. Data Knowl. Eng. 134, 101908 (2021). https://doi.org/10.1016/j.datak.2021.101908

    Article  Google Scholar 

  18. Rafiei, M., Wagner, M., van der Aalst, W.M.P.: TLKC-privacy model for process mining. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds.) RCIS 2020. LNBIP, vol. 385, pp. 398–416. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50316-1_24

    Chapter  Google Scholar 

  19. Rafiei, M., von Waldthausen, L., van der Aalst, W.M.P.: Supporting confidentiality in process mining using abstraction and encryption. In: Ceravolo, P., van Keulen, M., Gómez-López, M.T. (eds.) SIMPDA 2018-2019. LNBIP, vol. 379, pp. 101–123. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46633-6_6

    Chapter  Google Scholar 

  20. von Voigt, S.N., et al.: Quantifying the re-identification risk of event logs for process mining - empiricial evaluation paper. In: Advanced Information Systems Engineering, CAiSE (2020)

    Google Scholar 

  21. Wong, R.C.W., Fu, A.W.C., Wang, K., Pei, J.: Minimality attack in privacy preserving data publishing. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 543–554 (2007)

    Google Scholar 

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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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