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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2020
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| 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 |