Privacy Risk Assessment for Synthetic Longitudinal Health Data.
Schneider J, Walter M, Otte K, Meurers T, Perrar I, Nothlings U, Adams T, Frohlich H, Prasser F, Fluck J, Kühnel L (2024)
Studies in health technology and informatics 317: 270-279.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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SHTI-317-SHTI240867.pdf
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Autor*in
Schneider, Julian;
Walter, Marvin;
Otte, Karen;
Meurers, Thierry;
Perrar, Ines;
Nothlings, Ute;
Adams, Tim;
Frohlich, Holger;
Prasser, Fabian;
Fluck, Juliane;
Kühnel, LisaUniBi
Einrichtung
Abstract / Bemerkung
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.
Stichworte
Synthetic data;
Epidemiological study;
Privacy risk assessment;
Data sharing
Erscheinungsjahr
2024
Zeitschriftentitel
Studies in health technology and informatics
Band
317
Seite(n)
270-279
Urheberrecht / Lizenzen
ISSN
1879-8365
Page URI
https://pub.uni-bielefeld.de/record/2992360
Zitieren
Schneider J, Walter M, Otte K, et al. Privacy Risk Assessment for Synthetic Longitudinal Health Data. Studies in health technology and informatics. 2024;317:270-279.
Schneider, J., Walter, M., Otte, K., Meurers, T., Perrar, I., Nothlings, U., Adams, T., et al. (2024). Privacy Risk Assessment for Synthetic Longitudinal Health Data. Studies in health technology and informatics, 317, 270-279. https://doi.org/10.3233/SHTI240867
Schneider, Julian, Walter, Marvin, Otte, Karen, Meurers, Thierry, Perrar, Ines, Nothlings, Ute, Adams, Tim, et al. 2024. “Privacy Risk Assessment for Synthetic Longitudinal Health Data.”. Studies in health technology and informatics 317: 270-279.
Schneider, J., Walter, M., Otte, K., Meurers, T., Perrar, I., Nothlings, U., Adams, T., Frohlich, H., Prasser, F., Fluck, J., et al. (2024). Privacy Risk Assessment for Synthetic Longitudinal Health Data. Studies in health technology and informatics 317, 270-279.
Schneider, J., et al., 2024. Privacy Risk Assessment for Synthetic Longitudinal Health Data. Studies in health technology and informatics, 317, p 270-279.
J. Schneider, et al., “Privacy Risk Assessment for Synthetic Longitudinal Health Data.”, Studies in health technology and informatics, vol. 317, 2024, pp. 270-279.
Schneider, J., Walter, M., Otte, K., Meurers, T., Perrar, I., Nothlings, U., Adams, T., Frohlich, H., Prasser, F., Fluck, J., Kühnel, L.: Privacy Risk Assessment for Synthetic Longitudinal Health Data. Studies in health technology and informatics. 317, 270-279 (2024).
Schneider, Julian, Walter, Marvin, Otte, Karen, Meurers, Thierry, Perrar, Ines, Nothlings, Ute, Adams, Tim, Frohlich, Holger, Prasser, Fabian, Fluck, Juliane, and Kühnel, Lisa. “Privacy Risk Assessment for Synthetic Longitudinal Health Data.”. Studies in health technology and informatics 317 (2024): 270-279.
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