Efficient and Privacy Preserving Clustering Algorithm for Spatiotemporal Data

被引:0
作者
Mehmood, Abid [1 ]
Natgunanathan, Iynkaran [2 ]
Xiang, Yong [2 ]
机构
[1] Abu Dhabi Univ, Coll Engn, Abu Dhabi, U Arab Emirates
[2] Deakin Univ, Sch Informat Technol, Burwood, Vic, Australia
关键词
Clustering; privacy protection; spatiotemporal data; BIG DATA; MAPREDUCE; FREQUENT;
D O I
10.1142/S0219622022500110
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The efficiency of a spatiotemporal data analysis algorithm decreases as the amount of data increases. Many clustering techniques have been proposed for data analysis applications. However, applying those techniques to spatiotemporal data clustering is still in its infancy. In this paper, we tackle the issue of clustering spatiotemporal data on public Cloud based on the distance between them. To increase the efficiency of spatiotemporal clustering, we have proposed a MapReduce-based framework for clustering. However, as spatiotemporal dataset contains sensitive information, directly outsourcing spatiotemporal data to Cloud servers will raise privacy concerns. To address the problem of privacy, we have proposed a privacy preserving clustering algorithm based on MapReduce for spatiotemporal data that can be efficiently outsourced for data processing on the Cloud servers. The proposed scheme allows the clustering operation to be performed directly on the encrypted spatiotemporal data by Cloud server. Extensive experimental evaluation with trajectory data shows that our scheme efficiently produces higher quality clustering results.
引用
收藏
页码:967 / 992
页数:26
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