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.

Figure 1

Two types of network for distributed computing. The left panel is the network used for base GLORE model, in which only communications between server and clients are needed. The right panel is the network used for Algorithm 1, in which communications between two clients are also necessary.

Figure 2

The flow chart for Algorithm 2 for ROC curve computation. Suppose there are three local clients with local contingency tables A, B and C. By the secure summation (Algorithm 1), the central server get the overall table A+B+C.

Figure 3

ROC of a 2-site IPDLR for Edinburgh data

Figure 4

ROC of a 2-site IPDLR for CA-(19,125) data