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 asLeaper, to bridge the gap between location privacy protection and task allocation efficiency. In particular,Leaperoutputs 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,Leaperquantifies 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 thatLeaperindeed guarantees all desired properties of personalized differential privacy, truthfulness, individual rationality and budget feasibility.

User trajectory publication has promoted various location-based applications like user travel recommendation. However, possible privacy leakages have hindered more inclusive trajectory data analysis and utilization. Privacy-preserving trajectory synthesis is a popular approach to address the above privacy issues. Existing methods unavoidably produce low trajectory utility since they usually apply perturbed versions of human moving patterns. Worse still, they cannot adaptively adjust this synthesis according to the varying granularity demands of different users. This paper proposes a novel adaptive trajectory synthesis framework with generated data, namelyADGTrace. Our model achieves privacy preservation without introducing additional noise while maintaining high adaptation.ADGTracedirectly synthesizes artificial trajectories that share the similar patterns with real ones through agenerative and selectiveoptimization process. Additionally, we present a grid granularity alignment strategy to achieve adaptive trajectory synthesis, satisfying varying user demands. Extensive experiments on real-world datasets demonstrate the superiority ofADGTraceover the state-of-the art methods under various utility metrics, maintaining strong attack resilience.