Privacy risks of whole-slide image sharing in digital pathology

Authors

HOLUB Petr MÜLLER Heimo BÍL Tomáš PIREDDU Luca PLASS Markus PRASSER Fabian SCHLÜNDER Irene ZATLOUKAL Kurt NENUTIL Rudolf BRÁZDIL Tomáš

Year of publication 2023
Type Article in Periodical
Magazine / Source Nature Communications
MU Faculty or unit

Institute of Computer Science

Citation
Web https://www.nature.com/articles/s41467-023-37991-y
Doi http://dx.doi.org/10.1038/s41467-023-37991-y
Keywords digital pathology; whole slide images; anonymity; privacy risks; data sharing
Description 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.

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