  Privacy preserving data publishing has attracted considerable research
interest in recent years. Among the existing solutions, {\em
$\epsilon$-differential privacy} provides one of the strongest privacy
guarantees. Existing data publishing methods that achieve
$\epsilon$-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
$\epsilon$-differential privacy while providing accurate answers for {\em
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 {\em 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 extensive
experimental study on both real and synthetic data, we show the effectiveness
and efficiency of our solution.
