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Efficient and Privacy Preserving Clustering Algorithm for Spatiotemporal Data

journal contribution
posted on 2022-01-01, 00:00 authored by A Mehmood, Iynkaran NatgunanathanIynkaran Natgunanathan, Yong XiangYong Xiang
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.

History

Journal

International Journal of Information Technology and Decision Making

Pagination

1 - 26

Publisher

World Scientific

Location

Singapore

ISSN

0219-6220

eISSN

1793-6845

Language

eng

Publication classification

C1 Refereed article in a scholarly journal