iReduct: differential privacy with reduced relative errors

  • Authors:

  • Xiaokui Xiao;Gabriel Bender;Michael Hay;Johannes Gehrke

  • Affiliations:

  • Nanyang Technological University, Singapore, Singapore;Cornell University, Ithaca, USA;Cornell University, Ithaca, USA;Cornell University, Ithaca, USA

  • Venue:
  • Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2011

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Abstract

Prior work in differential privacy has produced techniques for answering aggregate queries over sensitive data in a privacy-preserving way. These techniques achieve privacy by adding noise to the query answers. Their objective is typically to minimize absolute errors while satisfying differential privacy. Thus, query answers are injected with noise whose scale is independent of whether the answers are large or small. The noisy results for queries whose true answers are small therefore tend to be dominated by noise, which leads to inferior data utility. This paper introduces iReduct, a differentially private algorithm for computing answers with reduced relative error. The basic idea of iReduct is to inject different amounts of noise to different query results, so that smaller (larger) values are more likely to be injected with less (more) noise. The algorithm is based on a novel resampling technique that employs correlated noise to improve data utility. Performance is evaluated on an instantiation of iReduct that generates marginals, i.e., projections of multi-dimensional histograms onto subsets of their attributes. Experiments on real data demonstrate the effectiveness of our solution.