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The Scientific World Journal
Volume 2014 (2014), Article ID 108072, 11 pages
http://dx.doi.org/10.1155/2014/108072
Research Article

Protecting Location Privacy for Outsourced Spatial Data in Cloud Storage

1School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2Shaanxi Province Key Laboratory of Computer Network, Xi’an Jiaotong University, Xi’an 710049, China

Received 25 February 2014; Accepted 16 June 2014; Published 6 July 2014

Academic Editor: Chang Wook Ahn

Copyright © 2014 Feng Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC and DSC are more secure than SHC, and DSC achieves the best index generation performance.