title: Preserving Institutional Privacy in Distributed binary Logistic Regression.
abstract: Privacy 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.
authors.affiliation: Division of Biomedical Informatics, Department of Medicine University of California San Diego, La Jolla 92093, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Wu
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D016494:Computer Security; D003219:Confidentiality; D006268:Health Facilities; D006801:Humans; D016015:Logistic Models; D018907:Privacy