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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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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Zhou, Y., Xiang, T., Li, X. (2018). Efficient and Privacy-Preserving Query on Outsourced Spherical Data. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_12
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