  We propose a practical methodology to protect a user's private data, when he
wishes to publicly release data that is correlated with his private data, in
the hope of getting some utility. Our approach relies on a general statistical
inference framework that captures the privacy threat under inference attacks,
given utility constraints. Under this framework, data is distorted before it is
released, according to a privacy-preserving probabilistic mapping. This mapping
is obtained by solving a convex optimization problem, which minimizes
information leakage under a distortion constraint. We address practical
challenges encountered when applying this theoretical framework to real world
data. On one hand, the design of optimal privacy-preserving mechanisms requires
knowledge of the prior distribution linking private data and data to be
released, which is often unavailable in practice. On the other hand, the
optimization may become untractable and face scalability issues when data
assumes values in large size alphabets, or is high dimensional. Our work makes
three major contributions. First, we provide bounds on the impact on the
privacy-utility tradeoff of a mismatched prior. Second, we show how to reduce
the optimization size by introducing a quantization step, and how to generate
privacy mappings under quantization. Third, we evaluate our method on three
datasets, including a new dataset that we collected, showing correlations
between political convictions and TV viewing habits. We demonstrate that good
privacy properties can be achieved with limited distortion so as not to
undermine the original purpose of the publicly released data, e.g.
recommendations.
