{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:09:24Z","timestamp":1782486564890,"version":"3.54.5"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030985806","type":"print"},{"value":"9783030985813","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":82,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1007\/978-3-030-98581-3_28","type":"book-chapter","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T18:03:23Z","timestamp":1648058603000},"page":"382-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quantifying the Re-identification Risk in Published Process Models"],"prefix":"10.1007","author":[{"given":"Karim","family":"Maatouk","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Mannhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"28_CR1","unstructured":"General Data Protection Regulation (GDPR) - Official Legal Text"},{"key":"28_CR2","unstructured":"van der Aalst, W.: Process Mining - Data Science in Action"},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/1472-6947-12-66","volume":"12","author":"FK Dankar","year":"2012","unstructured":"Dankar, F.K., El Emam, K., Neisa, A., Roffey, T.: Estimating the re-identification risk of clinical data sets. BMC Med. Inform. Decis. Making 12, 66 (2012)","journal-title":"BMC Med. Inform. Decis. Making"},{"key":"28_CR4","doi-asserted-by":"publisher","unstructured":"Domingo-Ferrer, J.: Disclosure risk. In: Liu, L., \u00d6zsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 848\u2013849. Springer, Boston (2009). https:\/\/doi.org\/10.1007\/978-0-387-39940-9_1506","DOI":"10.1007\/978-0-387-39940-9_1506"},{"key":"28_CR5","unstructured":"Elkoumy, G., Pankova, A., Dumas, M.: Privacy-preserving directly-follows graphs: balancing risk and utility in process mining (2020). arXiv:2012.01119"},{"issue":"4","key":"28_CR6","first-page":"307","volume":"62","author":"KE Emam","year":"2009","unstructured":"Emam, K.E., Dankar, F.K., Vaillancourt, R., Roffey, T., Lysyk, M.: Evaluating the risk of re-identification of patients from hospital prescription records. Can. J. Hosp. Pharm. 62(4), 307\u2013319 (2009)","journal-title":"Can. J. Hosp. Pharm."},{"key":"28_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-642-38697-8_17","volume-title":"Application and Theory of Petri Nets and Concurrency","author":"SJJ Leemans","year":"2013","unstructured":"Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311\u2013329. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38697-8_17"},{"key":"28_CR8","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-030-72693-5_29","volume-title":"Process Mining Workshops","author":"M Rafiei","year":"2021","unstructured":"Rafiei, M., van der Aalst, W.M.P.: Towards quantifying privacy in process mining. In: Leemans, S., Leopold, H. (eds.) ICPM 2020. LNBIP, vol. 406, pp. 385\u2013397. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72693-5_29"},{"key":"28_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-10933-3","volume":"10","author":"L Rocher","year":"2019","unstructured":"Rocher, L., Hendrickx, J., Montjoye, Y.A.: Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10, 1\u20139 (2019)","journal-title":"Nat. Commun."},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/978-3-030-49435-3_16","volume-title":"Advanced Information Systems Engineering","author":"S Nu\u00f1ez von Voigt","year":"2020","unstructured":"Nu\u00f1ez von Voigt, S., et al.: Quantifying the re-identification risk of event logs for process mining. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 252\u2013267. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49435-3_16"},{"issue":"11","key":"28_CR11","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/a13110279","volume":"13","author":"SJ van Zelst","year":"2020","unstructured":"van Zelst, S.J.: Translating workflow nets to process trees: an algorithmic approach. 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