Privacy risks of whole-slide image sharing in digital pathology.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
04 05 2023
04 05 2023
Historique:
received:
25
04
2022
accepted:
11
04
2023
medline:
8
5
2023
pubmed:
5
5
2023
entrez:
4
5
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37142591
doi: 10.1038/s41467-023-37991-y
pii: 10.1038/s41467-023-37991-y
pmc: PMC10160114
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2577Informations de copyright
© 2023. The Author(s).
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