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Ziller, A.* ; Mueller, T.T.* ; Stieger, S. ; Feiner, L.F.* ; Brandt, J.* ; Braren, R.* ; Rueckert, D.* ; Kaissis, G.

Reconciling privacy and accuracy in AI for medical imaging.

Nat. Mach. Intell., DOI: 10.1038/s42256-024-00858-y (2024)
DOI
Creative Commons Lizenzvertrag
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Artificial intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example, in medical imaging. Privacy-enhancing technologies, such as differential privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. Here we contrast the performance of artificial intelligence models at various privacy budgets against both theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP at all is negligent when applying artificial intelligence models to sensitive data. We deem our results to lay a foundation for further debates on striking a balance between privacy risks and model performance.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 2522-5839
e-ISSN 2522-5839
Zeitschrift Nature machine intelligence
Verlag Springer
Verlagsort [London]
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Förderungen German Academic Exchange Service (DAAD) under the Kondrad Zuse School of Excellence for Reliable AI (RelAI)
Bavarian Collaborative Research Project PRIPREKI of the Free State of Bavaria
Bavarian State Ministry for Science and the Arts through the Munich Centre for Machine Learning
Bavarian Cancer Research Center (BZKF, Lighthouse AI and Bioinformatics) - German Federal Ministry of Education and Research
Project 'NUM 2.0'
Federal Ministry of Education and Research (BMBF)
ERC Grant
German Ministry of Education and Research (BMBF)
Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)