EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning.

TitleEXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning.
Publication TypeJournal Article
Year of Publication2013
AuthorsWang, S, Jiang, X, Wu, Y, Cui, L, Cheng, S, Ohno-Machado, L
JournalJ Biomed Inform
Volume46
Issue3
Pagination480-96
Date Published2013 Jun
ISSN1532-0480
iDASH CategoryPrivacy Technology
AbstractWe developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications.
DOI10.1016/j.jbi.2013.03.008
Alternate JournalJ Biomed Inform
PubMed ID23562651