Valid statistical inference on automatically matched files

Authors:
Rob Hall;Stephen Fienberg
Affiliations:
Department of Statistics and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA;Department of Statistics and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA
Venue:
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Year:
2012

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Abstract

We develop a statistical process for determining a confidence set for an unknown bipartite matching. It requires only modest assumptions on the nature of the distribution of the data. The confidence set involves a set of linear constraints on the bipartite matching, which permits efficient analysis of the matched data, e.g., using linear regression, while maintaining the proper degree of uncertainty about the linkage itself.