A differentially private location generalization approach to guarantee non-uniform privacy in moving objects databases
作者：
Highlights：
• Differential privacy guarantees are combined with location generalization in a unified manner.
• Non-uniform privacy guarantees are achieved by generalizing locations while satisfying differential privacy.
• Scalability is preserved for spatial domains with a large number of locations.
• A quality improvement technique in a post-processing step is proposed to improve the quality of query answers.
• A new evaluation measure is defined to quantify the privacy protection provided by both location generalization and differential privacy.
摘要
•Differential privacy guarantees are combined with location generalization in a unified manner.•Non-uniform privacy guarantees are achieved by generalizing locations while satisfying differential privacy.•Scalability is preserved for spatial domains with a large number of locations.•A quality improvement technique in a post-processing step is proposed to improve the quality of query answers.•A new evaluation measure is defined to quantify the privacy protection provided by both location generalization and differential privacy.
论文关键词：Moving objects database,Trajectory,Differentially private location generalization,Differential privacy,Non-uniform privacy,Quality improvement
论文评审过程：Received 23 July 2020, Revised 22 April 2021, Accepted 24 April 2021, Available online 27 April 2021, Version of Record 8 May 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107084
