  For real-world mobile applications such as location-based advertising and
spatial crowdsourcing, a key to success is targeting mobile users that can
maximally cover certain locations in a future period. To find an optimal group
of users, existing methods often require information about users' mobility
history, which may cause privacy breaches. In this paper, we propose a method
to maximize mobile crowd's future location coverage under a guaranteed location
privacy protection scheme. In our approach, users only need to upload one of
their frequently visited locations, and more importantly, the uploaded location
is obfuscated using a geographic differential privacy policy. We propose both
analytic and practical solutions to this problem. Experiments on real user
mobility datasets show that our method significantly outperforms the
state-of-the-art geographic differential privacy methods by achieving a higher
coverage under the same level of privacy protection.
