HOLUB, Petr, Heimo MÜLLER, Tomáš BÍL, Luca PIREDDU, Markus PLASS, Fabian PRASSER, Irene SCHLÜNDER, Kurt ZATLOUKAL, Rudolf NENUTIL and Tomáš BRÁZDIL. Privacy risks of whole-slide image sharing in digital pathology. Nature Communications. 2023, vol. 14, No 2577, p. 1-15. ISSN 2041-1723. Available from: https://dx.doi.org/10.1038/s41467-023-37991-y.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Privacy risks of whole-slide image sharing in digital pathology
Authors HOLUB, Petr (203 Czech Republic, guarantor, belonging to the institution), Heimo MÜLLER, Tomáš BÍL (203 Czech Republic, belonging to the institution), Luca PIREDDU, Markus PLASS, Fabian PRASSER, Irene SCHLÜNDER, Kurt ZATLOUKAL, Rudolf NENUTIL (203 Czech Republic) and Tomáš BRÁZDIL (203 Czech Republic, belonging to the institution).
Edition Nature Communications, 2023, 2041-1723.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 16.600 in 2022
RIV identification code RIV/00216224:14610/23:00130727
Organization unit Institute of Computer Science
Doi http://dx.doi.org/10.1038/s41467-023-37991-y
UT WoS 001001562200003
Keywords in English digital pathology; whole slide images; anonymity; privacy risks; data sharing
Changed by Changed by: Mgr. Alena Mokrá, učo 362754. Changed: 5/4/2024 11:00.
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.
Links
90125, large research infrastructuresName: BBMRI-CZ III
PrintDisplayed: 20/7/2024 19:13