Toward Personalized Location Privacy Trading for Mobile Crowd Sensing

With the commercialization of private data, location privacy trading in Mobile Crowd Sensing (MCS) has become a fascinating research topic. In consideration of location-dependent sensing tasks, mobile workers take risks at location privacy disclosure when reporting their actual locations. Existing work fail to take workers’ diverse privacy protection and trading into account. This paper proposes a novel trading framework with personalized differential privacy guarantee, referred to as Leaper, to bridge the gap between location privacy protection and task allocation efficiency. In particular, Leaper outputs a personalized obfuscated range for each worker and further obfuscates his location based on a perturbation set within this range by incorporating differential privacy and k-anonymity techniques, and thus improves the efficiency of task allocation. Moreover, Leaper quantifies each worker’s location privacy loss and compensates him with reasonable payment by running auction in a cost-effective way. Through real-world datasets, our evaluations and analysis demonstrate that Leaper indeed guarantees all desired properties of personalized differential privacy, truthfulness, individual rationality and budget feasibility.