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For real-world mobile applications such as location-based advertising andspatial crowdsourcing, a key to success is targeting mobile users that canmaximally cover certain locations in a future period. To find an optimal groupof users, existing methods often require information about users' mobilityhistory, which may cause privacy breaches. In this paper, we propose a methodto maximize mobile crowd's future location coverage under a guaranteed locationprivacy protection scheme. In our approach, users only need to upload one oftheir frequently visited locations, and more importantly, the uploaded locationis obfuscated using a geographic differential privacy policy. We propose bothanalytic and practical solutions to this problem. Experiments on real usermobility datasets show that our method significantly outperforms thestate-of-the-art geographic differential privacy methods by achieving a highercoverage under the same level of privacy protection.

Geographic Differential Privacy for Mobile Crowd Coverage Maximization