  We consider the problem of obfuscating sensitive information while preserving
utility, and we propose a machine learning approach inspired by the generative
adversarial networks paradigm. The idea is to set up two nets: the generator,
that tries to produce an optimal obfuscation mechanism to protect the data, and
the classifier, that tries to de-obfuscate the data. By letting the two nets
compete against each other, the mechanism improves its degree of protection,
until an equilibrium is reached. We apply our method to the case of location
privacy, and we perform experiments on synthetic data and on real data from the
Gowalla dataset. We evaluate the privacy of the mechanism not only by its
capacity to defeat the classifier, but also in terms of the Bayes error, which
represents the strongest possible adversary. We compare the privacy-utility
tradeoff of our method to that of the planar Laplace mechanism used in
geo-indistinguishability, showing favorable results. Like the Laplace
mechanism, our system can be deployed at the user end for protecting his
location.
