Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Holub, P; Muller, H; Bil, T; Pireddu, L; Plass, M; Prasser, F; Schlunder, I; Zatloukal, K; Nenutil, R; Brazdil, T.
Privacy risks of whole-slide image sharing in digital pathology
NAT COMMUN. 2023; 14(1): 2577
Doi: 10.1038/s41467-023-37991-y
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- Co-Autor*innen der Med Uni Graz
- Müller Heimo
- Plass Markus
- Zatloukal Kurt
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- Abstract:
- Access to Whole-Slide Images has become a cornerstone of the development of AI methods in pathology, for diagnostic use and research. Authors have developed model for privacy risks analysis and propose guidelines for safe sharing of WSI data. Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle "as open as possible and as closed as necessary" is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.