Privacy risks of whole-slide image sharing in digital pathology

Petr Holub (), Heimo Müller, Tomáš Bíl, Luca Pireddu, Markus Plass, Fabian Prasser, Irene Schlünder, Kurt Zatloukal, Rudolf Nenutil and Tomáš Brázdil
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Petr Holub: BBMRI-ERIC
Heimo Müller: Medical University of Graz
Tomáš Bíl: Masaryk University
Luca Pireddu: CRS4
Markus Plass: Medical University of Graz
Fabian Prasser: Berlin Institute of Health @ Charité – Universitätsmedizin Berlin
Irene Schlünder: TMF eV
Kurt Zatloukal: Medical University of Graz
Rudolf Nenutil: BBMRI.cz & Masaryk Memorial Cancer Institute
Tomáš Brázdil: Masaryk University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-37991-y

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