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SIGMOD
2011
ACM

iReduct: differential privacy with reduced relative errors

7 years 9 months ago
iReduct: differential privacy with reduced relative errors
Prior work in differential privacy has produced techniques for answering aggregate queries over sensitive data in a privacypreserving 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 errors. 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 i...
Xiaokui Xiao, Gabriel Bender, Michael Hay, Johanne
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGMOD
Authors Xiaokui Xiao, Gabriel Bender, Michael Hay, Johannes Gehrke
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