Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables

Authors:
Stephen E. Fienberg;Alessandro Rinaldo;Xiaolin Yang
Affiliations:
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA and Cylab, and i-Lab, Carnegie Mellon University, P ...;Department of Statistics, Carnegie Mellon University, Pittsburgh, PA and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA;Department of Statistics, Carnegie Mellon University, Pittsburgh, PA
Venue:
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Year:
2010

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Abstract

The methodology of differential privacy has provided a strong definition of privacy which in some settings, using a mechanism of doubly-exponential noise addition, also allows for extraction of informative statistics from databases. A recent paper extends this approach to the release of a specified set of margins from a multi-way contingency table. Privacy protection in such settings implicitly focuses on small cell counts that might allow for the identification of units that are unique in the database. We explore how well the mechanism works in the context of a series of examples, and the extent to which the proposed differential-privacy mechanism allows for sensible inferences from the released data.