PRIPEL: Privacy-preserving event log publishing including contextual information


Fahrenkrog-Petersen, Stephan A. ; Aa, Han van der ; Weidlich, Matthias



DOI: https://doi.org/10.1007/978-3-030-58666-9_7
URL: https://link.springer.com/chapter/10.1007%2F978-3-...
Additional URL: https://arxiv.org/pdf/2006.12856.pdf
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: Business process management : 18th International Conference, BPM 2020, Seville, Spain, September 13–18, 2020, proceedings
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 12168
Page range: 111-128
Conference title: BPM 2020
Location of the conference venue: Online
Date of the conference: 13.-18.09.2020
Publisher: Fahland, Dirk ; Ghidini, Chiara ; Becker, Jörg ; Dumas, Marlon
Place of publication: Cham
Publishing house: Springer
ISBN: 978-3-030-58665-2 , 3-030-58665-0 , 978-3-030-58666-9
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Methoden der künstlichen Intelligenz (Juniorprofessur) (van der Aa 2020-)
Subject: 004 Computer science, internet
Abstract: Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals’ privacy. However, existing techniques for such pre-processing are limited to a process’ control-flow and neglect contextual information, such as attribute values and durations. This thus precludes any form of process analysis that involves contextual factors. To bridge this gap, we introduce PRIPEL, a framework for privacy-aware event log publishing. Compared to existing work, PRIPEL takes a fundamentally different angle and ensures privacy on the level of individual cases instead of the complete log. This way, contextual information as well as the long tail process behaviour are preserved, which enables the application of a rich set of process analysis techniques. We demonstrate the feasibility of our framework in a case study with a real-world event log.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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