Privacy risks of whole-slide image sharing in digital pathology
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F23%3A00079250" target="_blank" >RIV/00209805:_____/23:00079250 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/00216224:14610/23:00130727
Výsledek na webu
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160114/pdf/41467_2023_Article_37991.pdf" target="_blank" >https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160114/pdf/41467_2023_Article_37991.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41467-023-37991-y" target="_blank" >10.1038/s41467-023-37991-y</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Privacy risks of whole-slide image sharing in digital pathology
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Privacy risks of whole-slide image sharing in digital pathology
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
30109 - Pathology
Návaznosti výsledku
Projekt
<a href="/cs/project/LM2018125" target="_blank" >LM2018125: Banka klinických vzorků</a><br>
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Nature communications
ISSN
2041-1723
e-ISSN
2041-1723
Svazek periodika
14
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
16
Strana od-do
2577
Kód UT WoS článku
001001562200003
EID výsledku v databázi Scopus
2-s2.0-85158069987