Privacy-preserving model learning on a blockchain network-of-networks.

ABSTRACT:

Objective

To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.

Materials and methods

We propose a framework

SUBMITTER: Kuo TT 

PROVIDER: S-EPMC7025358 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature