Novel trajectory privacy-preserving method based on clustering using differential privacy
作者：
Highlights：
• Differential privacy technology is applied to trajectory clustering.
• Restricted noise is added to the location data to improve the clustering effect of noise data.
• The paper gives three attack models in cluster analysis and corresponding defense methods.
摘要
•Differential privacy technology is applied to trajectory clustering.•Restricted noise is added to the location data to improve the clustering effect of noise data.•The paper gives three attack models in cluster analysis and corresponding defense methods.
论文关键词：Trajectory data,Cluster analysis,Privacy protection,Differential privacy
论文评审过程：Received 12 October 2019, Revised 5 January 2020, Accepted 23 January 2020, Available online 1 February 2020, Version of Record 7 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113241
