Abstract
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.
Highlights
With the popularity of location-aware mobile devices, such as global position system (GPS) navigation or smart phones, Spatial Crowdsourcing (SC) [1] services have made daily life more convenient
In SC, users who are called requesters release their tasks to the spatial crowdsourcing server (SC-server), and other users who are called workers upload their location to the SC-server
Definition Local Differential Privacy on Location (LDPL) A mechanism K satisfies local differential privacy based on location (LDPL) if for all l, l, and d(l, l ) ≤ r: Dp K(l), K l ≤ d l, l where d(l, l ) is the Euclidean distance between l and l, and r is the radius of zone of privacy, and denote differential privacy parameter
Summary
With the popularity of location-aware mobile devices, such as global position system (GPS) navigation or smart phones, Spatial Crowdsourcing (SC) [1] services have made daily life more convenient. The SC-server assigns the tasks to the workers based on the locations of both the tasks and the workers. We propose a framework for protecting the privacy of workers’ locations in spatial crowdsourcing based on local differential privacy. Workers’ locations are obfuscated locally with local differential privacy and sent to SC-server for task assignment. The proposed framework does not require the participation of trusted third parties and workers can customize their privacy requirements. We propose a new framework that protects the location privacy of workers in SC. In comparison with other solutions, workers’ locations are obfuscated locally with noises in the proposed framework so that our framework does not require the participation of any trusted third parties for data collection and privacy processing, and the workers can customize their privacy requirements.
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