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Efficient and Privacy-Preserving Query on Outsourced Spherical Data

  • Yueyue Zhou
  • Tao Xiang
  • Xiaoguo Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

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.

Keywords

Outsourcing Privacy-preserving query Spherical data B\(^+\)-tree Hilbert curve 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61672118) and Graduate Scientific Research and Innovation Foundation of Chongqing, China (No. CYB16046).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer ScienceChongqing UniversityChongqingChina

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