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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...
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| Veröffentlicht in: | BMC Med Genomics |
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| Hauptverfasser: | , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
BioMed Central
2020
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7372765/ https://ncbi.nlm.nih.gov/pubmed/32693814 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12920-020-0723-0 |
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