Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

BACKGROUND: Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud beca...

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Detalles Bibliográficos
Autores principales: Carpov, Sergiu, Gama, Nicolas, Georgieva, Mariya, Troncoso-Pastoriza, Juan Ramon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372765/
https://www.ncbi.nlm.nih.gov/pubmed/32693814
http://dx.doi.org/10.1186/s12920-020-0723-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372765/
https://www.ncbi.nlm.nih.gov/pubmed/32693814
http://dx.doi.org/10.1186/s12920-020-0723-0

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