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We present new theoretical results on differentially private data releaseuseful with respect to any target class of counting queries, coupled withexperimental results on a variety of real world data sets.  Specifically, we study a simple combination of the multiplicative weightsapproach of [Hardt and Rothblum, 2010] with the exponential mechanism of[McSherry and Talwar, 2007]. The multiplicative weights framework allows us tomaintain and improve a distribution approximating a given data set with respectto a set of counting queries. We use the exponential mechanism to select thosequeries most incorrectly tracked by the current distribution. Combing the two,we quickly approach a distribution that agrees with the data set on the givenset of queries up to small error.  The resulting algorithm and its analysis is simple, but nevertheless improvesupon previous work in terms of both error and running time. We also empiricallydemonstrate the practicality of our approach on several data sets commonly usedin the statistical community for contingency table release.

A simple and practical algorithm for differentially private data release