Preserving Institutional Privacy in Distributed binary Logistic Regression.

TitlePreserving Institutional Privacy in Distributed binary Logistic Regression.
Publication TypeConference Proceedings
Year of Publication2012
AuthorsWu, Y, Jiang, X, Ohno-Machado, L
Conference NameAMIA Annu Symp Proc
Volume2012
Pagination1450-8
Date Published2012
ISBN1942-597X
iDASH CategoryPrivacy Technology
AbstractPrivacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.
PubMed ID23304425