Differentially private and utility-aware publication of trajectory data

作者：

Highlights：

• Propose two trajectory merging schemes based on k-means || clustering.

• Add bounded Staircase noise to the count of generalized trajectory to reduce error.

• Formally prove the proposed mechanisms satisfy differential privacy.

摘要

•Propose two trajectory merging schemes based on k-means || clustering.•Add bounded Staircase noise to the count of generalized trajectory to reduce error.•Formally prove the proposed mechanisms satisfy differential privacy.

论文关键词：Trajectory data,Differential privacy,Clustering,Staircase noise

论文评审过程：Received 18 March 2020, Revised 4 March 2021, Accepted 22 April 2021, Available online 28 April 2021, Version of Record 8 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115120