Toward Personalized Location Privacy Trading for Mobile Crowd Sensing

Summary

This paper proposes Leaper, a location privacy trading framework for Mobile Crowd Sensing (MCS) that provides personalized differential privacy. Leaper gives each worker a personalized obfuscated range and perturbs the worker's location within it, combining differential privacy with k-anonymity to improve task allocation efficiency. It quantifies each worker's location privacy loss and compensates workers through a cost-effective auction. Evaluations on real-world datasets show that Leaper guarantees personalized differential privacy, truthfulness, individual rationality and budget feasibility.