Eintrag weiter verarbeiten
Privacy-preserving model learning on a blockchain network-of-networks
Gespeichert in:
Personen und Körperschaften: | , , |
---|---|
Titel: |
Privacy-preserving model learning on a blockchain network-of-networks |
In: | Journal of the American Medical Informatics Association, 27, 2020, 3, S. 343-354 |
veröffentlicht: |
Oxford University Press (OUP)
|
Umfang: | 343-354 |
ISSN: |
1527-974X |
DOI: | 10.1093/jamia/ocz214 |
Zusammenfassung: | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Materials and Methods</jats:title> <jats:p>We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.</jats:p> </jats:sec> <jats:sec> <jats:title>Discussion</jats:title> <jats:p>HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.</jats:p> </jats:sec> |
Format: | E-Article |
Quelle: | Oxford University Press (OUP) (CrossRef) |
Sprache: | Englisch |