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Machine learning–based innovations can either strengthen or undermine data protection by altering identifiability risks. An IFIP Summer School workshop highlighted the field’s technical and legal complexity, emphasizing how a shared understanding of legal requirements and technical methods is necessary to guide responsible practice and support further interdisciplinary research.","University of Groningen  \nPrivacy-Enhancing Technologies and Anonymisation in Light of GDPR and Machine Learning  \nFischer-Hübner, Simone; Hansen, Marit; Hoepman, Jaap Henk; Jensen, Meiko  \nPublished in:  \nPrivacy and Identity Management  \nDOI:  \n10. 1007/978-3-031-31971-6_2  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nFischer-Hübner, S. , Hansen, M. , Hoepman, J. H. , & Jensen, M. (2023) . Privacy-Enhancing Technologies and Anonymisation in Light of GDPR and Machine Learning. In F. Bieker, J. Meyer, S. Pape, I. Schiering, &  \nA. Weich (Eds.), Privacy and Identity Management: 17th IFIP WG 9. 2, 9. 6/11. 7, 11. 6/SIG 9.2.2 International Summer School, Privacy and Identity 2022, Proceedings (pp. 11-20) . (IFIP Advances in Information and Communication Technology; Vol. 671 IFIP) . Springer Science and Business Media Deutschland GmbH. [https://doi.org/10.1007/978-3-031-31971-6_2](https://doi.org/10.1007/978-3-031-31971-6_2)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nPrivacy-Enhancing Technologies and Anonymisation in Light of GDPRand Machine Learning  \nSimone Fischer-Hu¨bner1 , Marit Hansen2 , Jaap-Henk Hoepman 1,3,4 ,  \nand Meiko Jensen1(B)  \n1 Karlstad University, Karlstad, Sweden  \n{simone.fischer-huebner,meiko.jensen}@kau .se  \n2 Unabha¨ngiges Landeszentrum fu¨r Datenschutz Schleswig-Holstein, Kiel, Germany [marit.hansen@datenschutzzentrum.de](marit.hansen@datenschutzzentrum.de)  \n3 Radboud University, Nijmegen, The Netherlands  \n[jhh@cs.ru.nl](jhh@cs.ru.nl)  \n4 University of Groningen, Groningen, The Netherlands  \n[j.h.hoepman@rug.nl](j.h.hoepman@rug.nl)  \nAbstract. The use of Privacy-Enhancing Technologies in the field of data anonymisation and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation. Here, especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection. A workshop held at the IFIP Summer School on Privacy and Identity Management showed the complexity of this field and the need for further interdisciplinary research on the basis of an improved joint understanding of legal and technical concepts.  \n1 Introduction  \nThe European General Data Protection Regulation (GDPR) regulates the processing of personal data. Anonymised data does not fall under its legal regime (cf. Recital 26 of the GDPR,[1]) . While the GDPR does not define the concept of “anonymisation”, Rec","cbCaimcWYJOHoCNm","https://ap.wps.com/l/cbCaimcWYJOHoCNm","pdf",1050334,1,11,"English","en",105,"# Abstract\n# Introduction\n## GDPR and anonymisation under Recital 26\n## Limits of anonymisation technologies","[{\"question\":\"How does GDPR treat anonymised data?\",\"answer\":\"Anonymised data falls outside GDPR’s legal regime when it constitutes information that does not relate to an identified or identifiable natural person, as clarified by Recital 26.\"},{\"question\":\"What role does Recital 26 play in defining identifiability?\",\"answer\":\"Recital 26 explains that identifiability depends on all means reasonably likely to be used, considering objective factors such as costs, time required, and available technology.\"},{\"question\":\"Why can machine learning-based anonymisation increase or decrease risk?\",\"answer\":\"Machine learning can change how identifiability is achieved or prevented, so it may reduce risks to data protection or create new pathways to re-identification, affecting whether data is sufficiently anonymised.\"}]","Privacy-Enhancing Technologies and Anonymisation in Light of GDPR and Machine Learning | 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does GDPR treat anonymised data?","Question",{"text":75,"@type":76},"Anonymised data falls outside GDPR’s legal regime when it constitutes information that does not relate to an identified or identifiable natural person, as clarified by Recital 26.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does Recital 26 play in defining identifiability?",{"text":80,"@type":76},"Recital 26 explains that identifiability depends on all means reasonably likely to be used, considering objective factors such as costs, time required, and available technology.",{"name":82,"@type":73,"acceptedAnswer":83},"Why can machine learning-based anonymisation increase or decrease risk?",{"text":84,"@type":76},"Machine learning can change how identifiability is achieved or prevented, so it may reduce risks to data protection or create new pathways to re-identification, affecting whether data is sufficiently 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