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...

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Detalles Bibliográficos
Autores principales: Paddock, Silvia, Abedtash, Hamed, Zummo, Jacqueline, Thomas, Samuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research Article
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

Internet

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

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