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Privacy-Preserving Process Mining

Differential Privacy for Event Logs

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Abstract

Privacy regulations for data can be regarded as a major driver for data sovereignty measures. A specific example for this is the case of event data that is recorded by information systems during the processing of entities in domains such as e-commerce or health care. Since such data, typically available in the form of event log files, contains personalized information on the specific processed entities, it can expose sensitive information that may be traced back to individuals. In recent years, a plethora of methods have been developed to analyse event logs under the umbrella of process mining. However, the impact of privacy regulations on the technical design as well as the organizational application of process mining has been largely neglected. This paper set out to develop a protection model for event data privacy which applies the well-established notion of differential privacy. Starting from common assumptions about the event logs used in process mining, this paper presents potential privacy leakages and means to protect against them. The paper also shows at which stages of privacy leakages a protection model for event logs should be used. Relying on this understanding, the notion of differential privacy for process discovery methods is instantiated, i.e., algorithms that aim at the construction of a process model from an event log. The general feasibility of our approach is demonstrated by its application to two publicly available real-life events logs.

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Notes

  1. Here, \(Range({\mathcal {K}})\) denotes the set of possible outputs of \({\mathcal {K}}\) and \(\Pr\) denotes probability.

  2. In the case of a combination of multiple attributes signalling the activity, we can always create a single attribute by concatenation of the multiple attributes.

  3. The source code of the privacy engine based on PINQ is available as C# application at: https://github.com/fmannhardt/pddp/.

  4. The keys for the partitioning operation need to be user-defined since we do not want to leak information on which keys are present in the unprotected event log.

  5. We chose the Inductive Miner since it is the only process discovery algorithm available in the open-source framework ProM 6.8 that allows to use both directly-follows relations and trace variants as input.

  6. https://data.4tu.nl/repository/collection:event_logs_real.

  7. The Petri net models are visualized using the compact Inductive Visual Miner notation as described in Leemans et al. (2014).

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Authors and Affiliations

  1. Department of Technology Management, SINTEF Digital, P.O. Box 4760, Torgarden, 7465, Trondheim, Norway

    Felix Mannhardt

  2. Department of Computer Science, Kiel University, Hermann-Rodewald-Str. 3, 24118, Kiel, Germany

    Agnes Koschmider

  3. IBM Almaden Research Center, San Jose, CA, USA

    Nathalie Baracaldo

  4. Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489, Berlin, Germany

    Matthias Weidlich

  5. Software Engineering, RWTH Aachen University, Ahornstraße 55, 52074, Aachen, Germany

    Judith Michael

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  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 Agnes Koschmider.

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Accepted after two revisions by the editors of the special edition.

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Mannhardt, F., Koschmider, A., Baracaldo, N. et al. Privacy-Preserving Process Mining. Bus Inf Syst Eng 61, 595–614 (2019). https://doi.org/10.1007/s12599-019-00613-3

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