Dp-CSM

Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks of poor scalability and suboptimal privacy–utility trade-off, due to continuous spatial space, high dimentionality of trajectory data and the suboptimal noise addition mechanism. To overcome the drawbacks, we propose DP-CSM, a novel differentially private trajectory generation method using coreset clustering and the staircase mechanism, to generate differentially private synthetic trajectories in two main steps. Firstly, it generates generalized locations for each timestamp, and utilizes coreset-based clustering to improve scalability. Secondly, it reconstructs synthetic trajectories with the generalized locations, and uses the staircase mechanism to avoid the over-perturbation of noises and maintain utility of synthetic trajectories. We choose three state-of-the-art clustering-based generation methods as the comparative baselines, and conduct comprehensive experiments on three real-world datasets to evaluate the performance of DP-CSM. Experimental results show that DP-CSM achieves better privacy–utility trade-off than the three baselines, and significantly outperforms the three baselines in terms of efficiency

Algorithm · Cluster analysis · Data mining · Database · Mathematical optimization · Real-time computing · Scalability · Spectral clustering · Synthetic data · Timestamp · Trajectory · Computer Science · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis · Mathematics · Privacy-Preserving Technologies in Data · Artificial Intelligence

• K-Anonymity

  Latanya Sweeney•International Journal of…•2002

  Add to list

• Limits of Predictability in Human Mobility

  Open Access•Chaoming Song, Zehui Qu et al.•Science•2010

  Add to list