Cargando…
Privacy-preserving logistic regression training
BACKGROUND: Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature...
Autores principales: | Bonte, Charlotte, Vercauteren, Frederik |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180357/ https://www.ncbi.nlm.nih.gov/pubmed/30309364 http://dx.doi.org/10.1186/s12920-018-0398-y |
Ejemplares similares
-
Privacy-preserving logistic regression with secret sharing
por: Ghavamipour, Ali Reza, et al.
Publicado: (2022) -
Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption
por: Carpov, Sergiu, et al.
Publicado: (2020) -
Towards practical privacy-preserving genome-wide association study
por: Bonte, Charlotte, et al.
Publicado: (2018) -
High performance logistic regression for privacy-preserving genome analysis
por: De Cock, Martine, et al.
Publicado: (2021) -
Privacy-preserving dataset combination and Lasso regression for healthcare predictions
por: van Egmond, Marie Beth, et al.
Publicado: (2021)