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Privacy-preserving Process Mining: Differential

Privacy for Event Logs (Extended Abstract)

  • HAUPTBEITRAG
  • PRIVACY-PRESERVING PROCESS MINING: DIFFERENTIAL
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Informatik Spektrum Aims and scope

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References

  1. D’Acquisto G, Domingo-Ferrer J, Kikiras P, Torra V, de Montjoye YA, Bourka A (2015) Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics

  2. Dwork C (2008) Differential privacy: A survey of results. In: International Conference on Theory and Applications of Models of Computation. Springer, pp 1–19

  3. Mannhardt F, Petersen S, de Oliveira MFD (2018) Privacy challenges for process mining in human-centered industrial environments. In: 14th International Conference on Intelligent Environments (IE). IEEE, Xplore, pp 64–71

  4. Mans RS, van der Aalst WMP, Vanwersch RJB, Moleman AJ (2013) Process mining in healthcare: Data challenges when answering frequently posed questions. In: Process Support and Knowledge Representation in Health Care. Springer, Berlin Heidelberg, pp 140–153

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Authors and Affiliations

  1. SINTEF Digital, Trondheim, Norway

    Felix Mannhardt

  2. Group Process Analytics, Kiel University, Kiel, Germany

    Agnes Koschmider

  3. IBM Almaden Research Center, San Jose, USA

    Nathalie Baracaldo

  4. Humboldt-Universität zu Berlin, 12489, Berlin, Germany

    Matthias Weidlich

  5. Software Engineering, RWTH Aachen University, Aachen, Germany

    Judith Michael

Authors
  1. Felix Mannhardt
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  2. Agnes Koschmider
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  3. Nathalie Baracaldo
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  4. Matthias Weidlich
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  5. Judith Michael
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Correspondence to Felix Mannhardt.

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Mannhardt, F., Koschmider, A., Baracaldo, N. et al. Privacy-preserving Process Mining: Differential. Informatik Spektrum 42, 349–351 (2019). https://doi.org/10.1007/s00287-019-01207-9

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  • DOI: https://doi.org/10.1007/s00287-019-01207-9

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