A simple and practical algorithm for differentially private data release

TL;DR

This paper introduces a simple, efficient algorithm combining multiplicative weights and the exponential mechanism for differentially private data release, achieving improved accuracy and practicality on real datasets.

Contribution

It presents a novel, practical algorithm that improves error bounds and runtime for differentially private data release using a combination of existing techniques.

Findings

Improved error bounds over previous methods

Faster runtime compared to prior algorithms

Effective on real-world data sets for contingency tables

Abstract

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