Cargando…
Towards realistic privacy-preserving deep learning over encrypted medical data
Cardiovascular disease supposes a substantial fraction of healthcare systems. The invisible nature of these pathologies demands solutions that enable remote monitoring and tracking. Deep Learning (DL) has arisen as a solution in many fields, and in healthcare, multiple successful applications exist...
Autores principales: | Cabrero-Holgueras, José, Pastrana, Sergio |
---|---|
Lenguaje: | eng |
Publicado: |
2023
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.3389/fcvm.2023.1117360 http://cds.cern.ch/record/2861961 |
Ejemplares similares
-
Towards realistic privacy-preserving deep learning over encrypted medical data
por: Cabrero-Holgueras, José, et al.
Publicado: (2023) -
Towards automated homomorphic encryption parameter selection with fuzzy logic and linear programming
por: Cabrero-Holgueras, José, et al.
Publicado: (2023) -
Encrypted email: the history and technology of message privacy
por: Orman, Hilarie
Publicado: (2015) -
Privacy-Preserving Data Publishing: An Overview
por: Wong, Raymond Chi-Wing, et al.
Publicado: (2010) -
Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning
por: Kurniawan, Hendra, et al.
Publicado: (2022)