Location Privacy Protection In Vehicle-Based Spatial Crowdsourcing Via Geo-Indistinguishability

Nowadays, vehicles have been increasingly adopted as participants in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location information to servers to facilitate the utilities of SC services. Traditional location privacy protection mechanisms cannot be directly applied to vehicle-based SC since they assume workers' location on a 2-dimensional plane, which does not take into account the features of vehicle workers' mobility in vehicle road networks. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) in vehicle road networks. Our objective is to design a location obfuscation strategy to minimize the loss of quality-of-service (QoS) due to task distribution with location obfuscation, while guaranteeing geo-indistinguishability to be satisfied. Considering the computational complexity of the VLP problem, by resorting to discretization, we approximate VLP to a linear programming problem that can be solved by existing well-developed approaches (such as the simplex method). To further improve the time efficiency, we reduce the number of constraints in VLP by exploiting key features of geo-indistinguishability in vechicle road networks (such as transitivity). Finally, our experimental results demonstrate that our approach can achieve a reasonable approximation of the minimum QoS loss with location privacy protected, and also outperforms a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.