Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine

Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine

Abstract 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 mo...

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Bibliographic Details
Main Authors: Silvia Paddock, Hamed Abedtash, Jacqueline Zummo, Samuel Thomas
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Homomorphic encryption
Learning system
Real-world evidence
Off-label treatment
Cancer
Online Access:https://doi.org/10.1186/s12911-019-0983-9

Internet

https://doi.org/10.1186/s12911-019-0983-9

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