Differential Privacy via Wavelet Transforms

9 years 11 months ago
Differential Privacy via Wavelet Transforms
Privacy preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, -differential privacy provides one of the strongest privacy guarantees. Existing data publishing methods that achieve differential privacy, however, offer little data utility. In particular, if the output dataset is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures -differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an exte...
Xiaokui Xiao, Guozhang Wang, Johannes Gehrke
Added 20 Dec 2009
Updated 03 Jan 2010
Type Conference
Year 2010
Where ICDE
Authors Xiaokui Xiao, Guozhang Wang, Johannes Gehrke
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