Quantifying the Re-identification Risk of Event Logs for Process Mining: Empiricial Evaluation Paper

Saskia Nuñez von Voigt, Stephan A. Fahrenkrog-Petersen, Dominik Janssen, Agnes Koschmider, Florian Tschorsch, Felix Mannhardt, Olaf Landsiedel, Matthias Weidlich

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

Abstract

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining.

Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering - 32nd International Conference, CAiSE 2020, Proceedings
EditorsSchahram Dustdar, Eric Yu, Vik Pant, Camille Salinesi, Dominique Rieu
PublisherSpringer
Pages252-267
Number of pages16
ISBN (Print)9783030494346
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020 - Grenoble, France
Duration: 8 Jun 202012 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12127 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020
CountryFrance
CityGrenoble
Period8/06/2012/06/20

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