{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:50:02Z","timestamp":1747216202406,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685366"}],"license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,30]]},"abstract":"<jats:p>Introduction: A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines. Methods: Therefore, the recent privacy risk quantification framework Anonymeter has been built for evaluating multiple possible vulnerabilities, which are specifically based on privacy risks that are considered by the European Data Protection Board, i.e. singling out, linkability, and attribute inference. This framework was applied to a synthetic data generation study from the epidemiological domain, where the synthesization replicates time and age trends previously found in data collected during the DONALD cohort study (1312 participants, 16 time points). The conducted privacy analyses are presented, which place a focus on the vulnerability of outliers. Results: The resulting privacy scores are discussed, which vary greatly between the different types of attacks. Conclusion: Challenges encountered during their implementation and during the interpretation of their results are highlighted, and it is concluded that privacy risk assessment for synthetic data remains an open problem.<\/jats:p>","DOI":"10.3233\/shti240867","type":"book-chapter","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:13:06Z","timestamp":1725527586000},"source":"Crossref","is-referenced-by-count":0,"title":["Privacy Risk Assessment for Synthetic Longitudinal Health Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3322-8672","authenticated-orcid":false,"given":"Julian","family":"Schneider","sequence":"first","affiliation":[{"name":"Knowledge Management, ZB MED \u2013 Information Centre for Life Sciences, Cologne, Germany"}]},{"given":"Marvin","family":"Walter","sequence":"additional","affiliation":[{"name":"Knowledge Management, ZB MED \u2013 Information Centre for Life Sciences, Cologne, Germany"}]},{"given":"Karen","family":"Otte","sequence":"additional","affiliation":[{"name":"Medical Informatics Group, Berlin Institute of Health at Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Berlin, Germany"}]},{"given":"Thierry","family":"Meurers","sequence":"additional","affiliation":[{"name":"Medical Informatics Group, Berlin Institute of Health at Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Berlin, Germany"}]},{"given":"Ines","family":"Perrar","sequence":"additional","affiliation":[{"name":"Institute of Nutritional and Food Sciences \u2013 Nutritional Epidemiology, University of Bonn, Bonn, Germany"}]},{"given":"Ute","family":"N\u00f6thlings","sequence":"additional","affiliation":[{"name":"Institute of Nutritional and Food Sciences \u2013 Nutritional Epidemiology, University of Bonn, Bonn, Germany"}]},{"given":"Tim","family":"Adams","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany"}]},{"given":"Holger","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany"}]},{"given":"Fabian","family":"Prasser","sequence":"additional","affiliation":[{"name":"Medical Informatics Group, Berlin Institute of Health at Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Berlin, Germany"}]},{"given":"Juliane","family":"Fluck","sequence":"additional","affiliation":[{"name":"Knowledge Management, ZB MED \u2013 Information Centre for Life Sciences, Cologne, Germany"},{"name":"The Agricultural Faculty, University of Bonn, Bonn, Germany"}]},{"given":"Lisa","family":"K\u00fchnel","sequence":"additional","affiliation":[{"name":"Knowledge Management, ZB MED \u2013 Information Centre for Life Sciences, Cologne, Germany"},{"name":"Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240867","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:13:07Z","timestamp":1725527587000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240867"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"ISBN":["9781643685366"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240867","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,30]]}}}