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PRETSA: Event log sanitization for privacy-aware process discovery
Fahrenkrog-Petersen, Stephan A.
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Aa, Han van der
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Weidlich, Matthias
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DOI:
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https://doi.org/10.1109/ICPM.2019.00012
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URL:
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https://ieeexplore.ieee.org/document/8786060
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Document Type:
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Conference or workshop publication
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Year of publication:
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2019
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Book title:
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2019 International Conference on Process Mining : Aachen, Germany, 24-26 June 2019, proceedings
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Page range:
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1-8
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Conference title:
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ICPM 2019
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Location of the conference venue:
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Aachen, Germany
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Date of the conference:
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24.-26.06.2019
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Place of publication:
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Piscataway, NJ
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Publishing house:
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IEEE
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ISBN:
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978-1-72810-920-6 , 978-1-72810-919-0
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Methoden der künstlichen Intelligenz (Juniorprofessur) (van der Aa 2020-)
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Subject:
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004 Computer science, internet
330 Economics
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Abstract:
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Event logs that originate from information systems enable comprehensive analysis of business processes, e.g., by process model discovery. However, logs potentially contain sensitive information about individual employees involved in process execution that are only partially hidden by an obfuscation of the event data. In this paper, we therefore address the risk of privacy-disclosure attacks on event logs with pseudonymized employee information. To this end, we introduce PRETSA, a novel algorithm for event log sanitization that provides privacy guarantees in terms of k-anonymity and t-closeness. It thereby avoids disclosure of employee identities, their membership in the event log, and their characterization based on sensitive attributes, such as performance information. Through step-wise transformations of a prefix-tree representation of an event log, we maintain its high utility for discovery of a performance-annotated process model. Experiments with real-world data demonstrate that sanitization with PRETSA yields event logs of higher utility compared to methods that exploit frequency-based filtering, while providing the same privacy guarantees.
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 | Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation. |
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