“Secure” log-linear and logistic regression analysis of distributed databases

Authors:
Stephen E. Fienberg;William J. Fulp;Aleksandra B. Slavkovic;Tracey A. Wrobel
Affiliations:
Department of Statistics, Carnegie Mellon University;Department of Statistics, Carnegie Mellon University;Department of Statistics, Pennsylvania State University;Department of Statistics, Pennsylvania State University
Venue:
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Year:
2006

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Abstract

The machine learning community has focused on confidentiality problems associated with statistical analyses that “integrate” data stored in multiple, distributed databases where there are barriers to simply integrating the databases. This paper discusses various techniques which can be used to perform statistical analysis for categorical data, especially in the form of log-linear analysis and logistic regression over partitioned databases, while limiting confidentiality concerns. We show how ideas from the current literature that focus on “secure” summations and secure regression analysis can be adapted or generalized to the categorical data setting.