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 more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. METHODS:We used published literature to estimate the numbers of patients who m

SUBMITTER: Paddock S 

PROVIDER: S-EPMC6894133 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature