Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation

Protecting patients’ privacy is one of the most important tasks when developing medical artificial intelligence models since medical data is the most sensitive personal data. To overcome this privacy protection issue, diverse privacy-preserving methods have been proposed. We proposed a novel method...

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Detalles Bibliográficos
Autores principales: Son, Yongha, Han, Kyoohyung, Lee, Yong Seok, Yu, Jonghan, Im, Young-Hyuck, Shin, Soo-Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687538/
https://www.ncbi.nlm.nih.gov/pubmed/34928973
http://dx.doi.org/10.1371/journal.pone.0260681

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687538/
https://www.ncbi.nlm.nih.gov/pubmed/34928973
http://dx.doi.org/10.1371/journal.pone.0260681

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