Privacy-preserving location data stream clustering on mobile edge computing and cloud
作者：
Highlights：
• Location data stream clustering requires advanced computing and storage capabilities.
• Edge–cloud interplay can provide a potential solution to this problem.
• The privacy of location data is another major issue that limits the effective use of cloud resources.
• It is essential to look at both efficiency and privacy of location data stream clustering.
• An efficient distributed edge–cloud scenario for location data clustering with differential privacy is proposed.
摘要
•Location data stream clustering requires advanced computing and storage capabilities.•Edge–cloud interplay can provide a potential solution to this problem.•The privacy of location data is another major issue that limits the effective use of cloud resources.•It is essential to look at both efficiency and privacy of location data stream clustering.•An efficient distributed edge–cloud scenario for location data clustering with differential privacy is proposed.
论文关键词：Mobile edge computing,Big data,Location-based services,Differential privacy,Cluster analysis,Privacy-preserving location-based services,Data stream privacy
论文评审过程：Received 29 June 2020, Revised 11 November 2020, Accepted 24 January 2021, Available online 10 February 2021, Version of Record 26 March 2022.
论文官网地址：https://doi.org/10.1016/j.is.2021.101728
