Skip to main content
SpringerLink
Log in
Book cover

International Conference on Business Process Management

BPM 2022: Business Process Management pp 219–233Cite as

The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13420))

Abstract

Over the past decade, process mining has emerged as a new area of research focused on analyzing end-to-end processes through the use of event data and novel techniques for process discovery and conformance testing. While the benefits of process mining are widely recognized scientifically, research has increasingly addressed privacy concerns regarding the use of personal data and sensitive information that requires protection and compliance with data protection regulations. However, the privacy debate is currently answered exclusively by technical safeguards that lead to the anonymization of process data. This research analyzes the real-world utility of these process data anonymization techniques and evaluates their suitability for privacy protection. To this end, we use process mining in a case study to investigate how responsible users and specific user groups can be identified despite the technical anonymization of process mining data.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare – a case study in a dutch hospital. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 425–438. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-92219-3_32

    Chapter  Google Scholar 

  2. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19

    Chapter  Google Scholar 

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

    Book  Google Scholar 

  4. Rovani, M., Maggi, F.M., de Leoni, M., van der Aalst, W.M.: Declarative process mining in healthcare. Expert Syst. Appl. 42, 9236–9251 (2015). https://doi.org/10.1016/j.eswa.2015.07.040

    Article  Google Scholar 

  5. Fredriksen, E., Martinez, S., Moe, C.E., Thygesen, E.: Communication and information exchange between primary healthcare employees and volunteers - challenges, needs and possibilities for technology support. Health Soc. Care Community 28, 1252–1260 (2020). https://doi.org/10.1111/hsc.12958

    Article  Google Scholar 

  6. Ghasemi, M., Amyot, D.: Process mining in healthcare: a systematised literature review. Int. J. Electron. Healthc. 72 (2016). https://doi.org/10.1504/IJEH.2016.078745

  7. Erdogan, T.G., Tarhan, A.: A goal-driven evaluation method based on process mining for healthcare processes. Appl. Sci. 8, 894 (2018). https://doi.org/10.3390/app8060894

    Article  Google Scholar 

  8. Mans, R., Reijers, H., Wismeijer, D., van Genuchten, M.: A process-oriented methodology for evaluating the impact of IT: a proposal and an application in healthcare. Inf. Syst. 38, 1097–1115 (2013). https://doi.org/10.1016/j.is.2013.06.005

    Article  Google Scholar 

  9. Martin, N., et al.: Recommendations for enhancing the usability and understandability of process mining in healthcare. Artif. Intell. Med. 109, 101962 (2020)

    Article  Google Scholar 

  10. Munoz-Gama, J., et al.: Process mining for healthcare: characteristics and challenges. J. Biomed. Inform. 127, 103994 (2022). https://doi.org/10.1016/j.jbi.2022.103994

    Article  Google Scholar 

  11. Eggers, J., Hein, A., Böhm, M., Krcmar, H.: No longer out of sight, no longer out of mind? How organizations engage with process mining-induced transparency to achieve increased process awareness. Bus. Inf. Syst. Eng. 63(5), 491–510 (2021). https://doi.org/10.1007/s12599-021-00715-x

    Article  Google Scholar 

  12. Grisold, T., Mendling, J., Otto, M., vom Brocke, J.: Adoption, use and management of process mining in practice. BPMJ 27, 369–387 (2020)

    Article  Google Scholar 

  13. Pika, A., Wynn, M.T., Budiono, S., ter Hofstede, A.H.M., van der Aalst, W.M.P., Reijers, H.A.: Privacy-preserving process mining in healthcare. Int. J. Environ. Res. Public Health 17, 1612 (2020). https://doi.org/10.3390/ijerph17051612

    Article  Google Scholar 

  14. Mannhardt, F., Koschmider, A., Baracaldo, N., Weidlich, M., Michael, J.: Privacy-preserving process mining. Bus. Inf. Syst. Eng. 61, 595–614 (2019). https://doi.org/10.1007/s12599-019-00613-3

    Article  Google Scholar 

  15. vom Brocke, J., Jans, M., Mendling, J., Reijers, H.A.: A five-level framework for research on process mining. Bus. Inf. Syst. Eng. 63(5), 483–490 (2021). https://doi.org/10.1007/s12599-021-00718-8

    Article  Google Scholar 

  16. 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 

  17. Loxton, M.: Process Mining in Healthcare. (2016, Unpublished)

    Google Scholar 

  18. Weise, M., Kovacevic, F., Popper, N., Rauber, A.: OSSDIP: open source secure data infrastructure and processes supporting data visiting. Data Sci. J. 21 (2022). https://doi.org/10.5334/dsj-2022-004

  19. Nuñez von Voigt, S., et al.: Quantifying the re-identification risk of event logs for process mining. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 252–267. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49435-3_16

    Chapter  Google Scholar 

  20. Houghton, C., Casey, D., Shaw, D., Murphy, K.: Rigour in qualitative case-study research. Nurse Res. (2013)

    Google Scholar 

  21. Murthy, S., et al.: A comparative study of data anonymization techniques. In: 5th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance (2019)

    Google Scholar 

  22. Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016). https://doi.org/10.1016/j.jbi.2016.04.007

    Article  Google Scholar 

  23. Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B.: Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16071-9

    Book  Google Scholar 

  24. Mans, R.S., Schonenberg, M.H., Leonardi, G., Panzarasa, S., Quaglini, S., van der Aalst, W.M.P.: Process mining techniques: an application to stroke care. Stud. Helath Technol. Inform. 136, 573–578 (2008)

    Google Scholar 

  25. van der Aalst, W.M.P., Günther, C.W.: Finding structure in unstructured processes: the case for process mining. In: Basten, T., Juhás, G., Shukla, S.K. (eds.) Seventh International Conference on Application of Concurrency to System Design, ACSD 2007, Proceedings, Bratislava, Slovak Republic, 10–13 July 2007. IEEE Computer Society, Los Alamitos (2007). https://doi.org/10.1109/acsd.2007.50

  26. Yin, R.K.: Case Study Research and Applications: Design and Methods. SAGE Publications, Thousand Oaks (2017)

    Google Scholar 

  27. Yin, R.K.: Qualitative Research from Start to Finish. Guilford Publications, New York (2015)

    Google Scholar 

  28. Dongen, B.F., Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25

    Chapter  Google Scholar 

  29. Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Xes, xesame, and prom 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-17722-4_5

    Chapter  Google Scholar 

  30. Günther, C.W., van der Aalst, W.M.P.: A generic import framework for process event logs. In: Eder, J., Dustdar, S. (eds.) Business Process Management Workshops, pp. 81–92. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https://doi.org/10.1007/11837862_10

    Chapter  Google Scholar 

  31. Flick, U.: Triangulation. In: Mey, G., Mruck, K. (eds.) Handbuch Qualitative Forschung in der Psychologie, pp. 185–199. Springer, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-26887-9_23

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. South Westphalia University of Applied Sciences, Haldener Str. 182, 58095, Hagen, Germany

    Friederike Maria Bade, Carolin Vollenberg, Jannis Koch, Julian Koch & Andre Coners

Authors
  1. Friederike Maria Bade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carolin Vollenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jannis Koch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julian Koch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andre Coners
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Friederike Maria Bade .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Claudio Di Ciccio

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco Dijkman

  3. Universidad de Sevilla, Seville, Spain

    Adela del Río Ortega

  4. Technical University of Munich, Garching, Germany

    Stefanie Rinderle-Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bade, F.M., Vollenberg, C., Koch, J., Koch, J., Coners, A. (2022). The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. Lecture Notes in Computer Science, vol 13420. Springer, Cham. https://doi.org/10.1007/978-3-031-16103-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16103-2_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16102-5

  • Online ISBN: 978-3-031-16103-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation