Cargando…
Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis
BACKGROUND: There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently wi...
Autores principales: | Lee, Junghye, Sun, Jimeng, Wang, Fei, Wang, Shuang, Jun, Chi-Hyuck, Jiang, Xiaoqian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5924379/ https://www.ncbi.nlm.nih.gov/pubmed/29653917 http://dx.doi.org/10.2196/medinform.7744 |
Ejemplares similares
-
Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources
por: Huang, Yingxiang, et al.
Publicado: (2018) -
Privacy-preserving GWAS analysis on federated genomic datasets
por: Constable, Scott D, et al.
Publicado: (2015) -
Privacy‐preserving quality control of neuroimaging datasets in federated environments
por: Saha, Debbrata K., et al.
Publicado: (2022) -
Privacy-preserving federated genome-wide association studies via dynamic sampling
por: Wang, Xinyue, et al.
Publicado: (2023) -
Preserving Differential Privacy for Similarity Measurement in Smart Environments
por: Wong, Kok-Seng, et al.
Publicado: (2014)