Geographic Differential Privacy for Mobile Crowd Coverage Maximization
This work addresses privacy concerns in mobile applications like location-based advertising and spatial crowdsourcing, offering a practical solution for users and service providers, though it is incremental as it builds on existing geographic differential privacy methods.
The paper tackles the problem of maximizing future location coverage for mobile crowds while ensuring location privacy, by having users upload obfuscated frequently visited locations under geographic differential privacy. Experiments on real datasets show that their method significantly outperforms state-of-the-art geographic differential privacy methods by achieving higher coverage under the same privacy level.
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.