Output perturbation with query relaxation

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Output perturbation with query relaxation
Given a dataset containing sensitive personal information, a statistical database answers aggregate queries in a manner that preserves individual privacy. We consider the problem of constructing a statistical database using output perturbation, which protects privacy by injecting a small noise into each query result. We show that the state-of-the-art approach, -differential privacy, suffers from two severe deficiencies: it (i) incurs prohibitive computation overhead, and (ii) can answer only a limited number of queries, after which the statistical database has to be shut down. To remedy the problem, we develop a new technique that enforces -different privacy with economical cost. Our technique also incorporates a query relaxation mechanism, which removes the restriction on the number of permissible queries. The effectiveness and efficiency of our solution are verified through experiments with real data.
Xiaokui Xiao, Yufei Tao
Added 28 Dec 2010
Updated 28 Dec 2010
Type Journal
Year 2008
Authors Xiaokui Xiao, Yufei Tao
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