Trajectory Hiding and Sharing for Supply Chains with Differential Privacy

With the fast development of e-commerce, there is a higher demand for timely delivery. Logistic companies want to send receivers a more accurate arrival prediction to improve customer satisfaction and lower customer retention costs. One approach is to share (near) real-time location data with recipients, but this also introduces privacy and security issues such as malicious tracking and theft. In this paper, we propose a privacy-preserving real-time location sharing system including (1) a differential privacy based location publishing method and (2) location sharing protocols for both centralized and decentralized platforms. Different from existing location perturbation solutions which only consider privacy in theory, our location publishing method is based on a real map and different privacy levels for recipients. Our analyses and proofs show that the proposed location publishing method provides better privacy protection than existing works under real maps against possible attacks. We also provide a detailed analysis of the choice of the privacy parameter and their impact on the suggested noisy location outputs. The experimental results demonstrate that our proposed method is feasible for both centralized and decentralized systems and can provide more precise arrival prediction than using time slots in current delivery systems.