TY - GEN
T1 - Quantifying the Re-identification Risk of Event Logs for Process Mining
T2 - 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020
AU - Nuñez von Voigt, Saskia
AU - Fahrenkrog-Petersen, Stephan A.
AU - Janssen, Dominik
AU - Koschmider, Agnes
AU - Tschorsch, Florian
AU - Mannhardt, Felix
AU - Landsiedel, Olaf
AU - Weidlich, Matthias
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086228997&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49435-3_16
DO - 10.1007/978-3-030-49435-3_16
M3 - Conference contribution
AN - SCOPUS:85086228997
SN - 9783030494346
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 267
BT - Advanced Information Systems Engineering - 32nd International Conference, CAiSE 2020, Proceedings
A2 - Dustdar, Schahram
A2 - Yu, Eric
A2 - Pant, Vik
A2 - Salinesi, Camille
A2 - Rieu, Dominique
PB - Springer
Y2 - 8 June 2020 through 12 June 2020
ER -