Title: Utility Preserving Secure Private Data Release,
Abstract: Differential privacy mechanisms that also make reconstruction of the data
impossible come at a cost - a decrease in utility. In this paper, we tackle
this problem by designing a private data release mechanism that makes
reconstruction of the original data impossible and also preserves utility for a
wide range of machine learning algorithms. We do so by combining the
Johnson-Lindenstrauss (JL) transform with noise generated from a Laplace
distribution. While the JL transform can itself provide privacy guarantees
\cite{blocki2012johnson} and make reconstruction impossible, we do not rely on
its differential privacy properties and only utilize its ability to make
reconstruction impossible. We present novel proofs to show that our mechanism
is differentially private under single element changes as well as single row
changes to any database. In order to show utility, we prove that our mechanism
maintains pairwise distances between points in expectation and also show that
its variance is proportional to the the dimensionality of the subspace we
project the data into. Finally, we experimentally show the utility of our
mechanism by deploying it on the task of clustering.