2017/10/28 by Leye Wang, Gehua Qin, Wang, Leye +7 · 1 citation
Computer Science · Social Sciences · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Mobile Crowdsensing and Crowdsourcing #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1710.10477
openalex publication_date 2017/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.