A Privacy Preserving Framework for Worker's Location in Spatial Crowdsourcing Based on Local Differential Privacy

With the development of the mobile Internet, location-based services are playing an important role in everyday life. As a new location-based service, Spatial Crowdsourcing (SC) involves collecting and analyzing environmental, social, and other spatiotemporal information of individuals, increasing convenience for users. In SC, users (called requesters) publish tasks and other users (called workers) are required to physically travel to specified locations to perform the tasks. However, with SC services, the workers have to disclose their locations to untrusted third parties, such as the Spatial Crowdsourcing Server (SC-server), which could pose a considerable threat to the privacy of workers. In this paper, we propose a new location privacy protection framework based on local difference privacy for spatial crowdsourcing, which does not require the participation of trusted third parties by adding noises locally to workers' locations. The noisy locations of workers are submitted to the SC-server rather than the real locations. Therefore, the protection of workers' locations is achieved. Experiments showed that this framework not only preserves the privacy of workers in SC, but also has modest overhead performance.