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2020 | OriginalPaper | Chapter

TLKC-Privacy Model for Process Mining

Authors : Majid Rafiei, Miriam Wagner, Wil M. P. van der Aalst

Published in: Research Challenges in Information Science

Publisher: Springer International Publishing

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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. Consider for example health-care information systems recording highly sensitive data related to diagnosis and treatment activities. Process mining should reveal insights in the form of annotated models, yet, at the same time, should not reveal sensitive information about individuals. In this paper, we discuss the challenges regarding directly applying existing well-known privacy-preserving techniques to event data. We introduce the TLKC-privacy model for process mining that provides privacy guarantees in terms of group-based anonymization. It extends and customizes the LKC-privacy model presented to deal with high-dimensional, sparse, and sequential trajectory data. Experiments on real-life event data demonstrate that our privacy model maintains a high utility for process discovery and performance analyses while preserving the privacy of the cases.

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previous chapter Discovering Business Process Simulation Models in the Presence of Multitasking

next chapter Incremental Discovery of Hierarchical Process Models

https://github.com/Widderiru/TLKC-privacy/tree/master/home_version.

These results have been provided by Disco (https://fluxicon.com/disco/) with the sliders set to the maximal number of activities and the minimal paths.

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Title: TLKC-Privacy Model for Process Mining
Authors: Majid Rafiei
Miriam Wagner
Wil M. P. van der Aalst
Publisher: Springer International Publishing
Book: Research Challenges in Information Science
Print ISBN: 978-3-030-50315-4

Electronic ISBN: 978-3-030-50316-1

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-50316-1_24

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