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Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine
BACKGROUND: The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more wides...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894133/ https://www.ncbi.nlm.nih.gov/pubmed/31801535 http://dx.doi.org/10.1186/s12911-019-0983-9 |