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2018 | OriginalPaper | Chapter

Efficient and Privacy-Preserving Query on Outsourced Spherical Data

Authors : Yueyue Zhou, Tao Xiang, Xiaoguo Li

Published in: Algorithms and Architectures for Parallel Processing

Publisher: Springer International Publishing

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Abstract

Outsourcing spatial database to the cloud becomes a paradigm for many applications such as location-bases service (LBS). At the same time, the security of outsourced data and its query becomes a serious issue. In this paper, we consider 3D spherical data that has wide applications in geometric information systems (GIS), and investigate its privacy-preserving query problem. By using an approximately distance-preserving 3D-2D projection method, we first project 3D spatial points to six possible 2D planes. Then we utilize secure Hilbert space-filling curve to encode the 2D points into 1D Hilbert values. After that, we build an encrypted spatial index tree using B\(^+\)-tree and order-preserving encryption (OPE). Our scheme supports efficient point query, arbitrary polygon query, as well as dynamic updating in the encrypted domain. Theoretical analysis and experimental results on real-word datasets demonstrate its satisfactory tradeoff between security and efficiency.

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Real spatial datasets. http://www.cs.utah.edu/~lifeifei/SpatialDataset.htm

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Title: Efficient and Privacy-Preserving Query on Outsourced Spherical Data
Authors: Yueyue Zhou
Tao Xiang
Xiaoguo Li
Publisher: Springer International Publishing
Book: Algorithms and Architectures for Parallel Processing
Print ISBN: 978-3-030-05062-7

Electronic ISBN: 978-3-030-05063-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-05063-4_12

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