Quantifying the Re-identification Risk in Published Process Models.

Karim Maatouk, Felix Mannhardt

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)


Event logs are the basis of process mining operations such as process discovery, conformance checking, and process optimization. Sensitive information may be obtained by adversaries when re-identifying individuals that relate to the traces of an event log. This re-identification risk is dependent on the assumed background information of an attacker. Multiple techniques have been proposed to quantify the re-identification risks for published event logs. However, in many scenarios there is no need to release the full event log, a discovered process model annotated with frequencies suffices. This raises the question on how to quantify the re-identification risk in published process models. We propose a method based on generating sample traces to quantify this risk for process trees annotated with frequencies. The method was applied on several real-life event logs and process trees discovered by Inductive Miner. Our results show that there can be still a significant re-identification risk when publishing a process tree; however, this risk is often lower than that for releasing the original event log.

Originele taal-2Engels
TitelProcess Mining Workshops - ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 - November 4, 2021, Revised Selected Papers
RedacteurenJorge Munoz-Gama, Xixi Lu
Aantal pagina's13
ISBN van elektronische versie978-3-030-98580-6
ISBN van geprinte versie9783030985806
StatusGepubliceerd - 2022

Publicatie series

NaamLecture Notes in Business Information Processing (LNBIP)
ISSN van geprinte versie1865-1348
ISSN van elektronische versie1865-1356

Bibliografische nota

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