The widespread use of location-based services (LBSs) has led to the continuous collection of user location and trajectory data, increasing the risk of privacy leakage and creating a persistent tradeoff between privacy protection and data utility. To address this problem in discrete location query scenarios, this paper...
The augmented multi-party shuffle DP (AMP-SDP) model is proposed, which re-architects the data pipeline with a lightweight, versatile secret-shared intermediary layer that decentralizes trust while minimizing online communication costs and provides structural security hardening against both shuffler compromise and user-sid...
A PWS framework called UPS is proposed that aims at maintaining equilibrium between two predictive metrics that gauges the utility of personalization and the privacy risk of uncovering the generalized profile, and GreedyDP and GreedyIL are the two greedy algorithms for runtime generalization.
Ensuring robust privacy for locations, trajectories, and mobility patterns in Location-Based Services remains a critical challenge, as existing anonymization techniques often fail to protect these three dimensions simultaneously and typically neglect Point of Interest (POI) awareness and population-density adaptation...
The widespread adoption of Location-Based Services (LBSs) has significantly enhanced daily convenience, yet the upload and storage of user trajectory data pose substantial privacy leakage risks. Addressing the limitations of existing privacy protection methods in achieving personalized adaptation and balancing privacy...
The advent of location-aware gadgets and amenities has resulted in an unprecedented scale of trajectory data being generated, gathered and analyzed in a variety of sectors including urban planning, transportation management, monitoring public health, and corporate analytics. However, trajectory data has inherent spatia...
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