Preserving Institutional Privacy in Distributed binary Logistic Regression.

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Wu, Y; Jiang, X; Ohno-Machado, L

Published Date

  • 2012

Published In

Volume / Issue

  • 2012 /

Start / End Page

  • 1450 - 1458

PubMed ID

  • 23304425

Pubmed Central ID

  • PMC3540539

Electronic International Standard Serial Number (EISSN)

  • 1942-597X

Conference Location

  • United States