Patient Privacy, Big Data, and PoLoR: Using an Old Tool for New Challenges

• Activity Number: 125
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Paramita Saha Chaudhuri*
• Companies: McGill University
• Keywords: Distributed data ; Patient privacy ; PoLoR (Pooled Logistic Regression)

Abstract

Lately, electronic health records and distributed data networks emerged as viable resource for medical and scientific research. As the use of confidential patient information from such sources become more common, maintaining privacy of patients is of importance. For a binary disease outcome of interest, we apply specimen pooling and Pooled Logistic Regression (PoLoR) for analysis of large and/or distributed data while respecting patient privacy. PoLoR is the same as standard logistic regression, but instead of using individual covariate level, the analysis uses pooled or aggregate covariate level when pooling is conditional on the outcome status. Aggregate levels of covariates can be passed from the nodes of the network to the analysis center without revealing individual covariate level and can be used very easily with logistic regression for estimation of disease odds ratio associated with a set of categorical or continuous covariates. Since pooling effectively reduces the size of the dataset by creating pools or sets of individual, the resulting dataset can be analyzed much more quickly as compared to an original dataset that is too big as compared to computing environment.