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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References
Real spatial datasets. http://www.cs.utah.edu/~lifeifei/SpatialDataset.htm
S2Geometry. http://s2geometry.io/
Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Order preserving encryption for numeric data. In: Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD), pp. 563–574 (2004)
Boldyreva, A., Chenette, N., Lee, Y., O’Neill, A.: Order-preserving symmetric encryption. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 224–241. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01001-9_13
Hilbert, D.: Über die stetige abbildung einer linie auf ein flächenstück. Math. Ann. 38, 459–460 (1891)
Kamel, I., Talha, A.M., Aghbari, Z.A.: Dynamic spatial index for efficient query processing on the cloud. J. Cloud Comput. 6(1), 5 (2017)
Khoshgozaran, A., Shahabi, C.: Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 239–257. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73540-3_14
Kim, H.I., Hong, S.T., Chang, J.W.: Hilbert-curve based cryptographic transformation scheme for protecting data privacy on outsourced private spatial data, pp. 77–82 (2014)
Ku, W.-S., Hu, L., Shahabi, C., Wang, H.: Query integrity assurance of location-based services accessing outsourced spatial databases. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds.) SSTD 2009. LNCS, vol. 5644, pp. 80–97. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02982-0_8
Lewi, K., Wu, D.J.: Order-revealing encryption: new constructions, applications, and lower bounds. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1167–1178 (2016)
Lung, M.Y., Ghinita, G., Jensen, C.S., Kalnis, P.: Outsourcing search services on private spatial data, pp. 1140–1143 (2009)
Lung, M.Y., Ghinita, G., Jensen, C.S., Kalnis, P.: Enabling search services on outsourced private spatial data. VLDB J. 19(3), 363–384 (2010)
Luo, Y., Fu, S., Wang, D., Xu, M., Jia, X.: Efficient and generalized geometric range search on encrypted spatial data in the cloud. In: IEEE/ACM Conference on Quality of Service (IWQoS), pp. 1–10 (2017)
Ren, H., Li, H., Chen, H., Kpiebaareh, M., Zhao, L.: Efficient privacy-preserving circular range search on outsourced spatial data. In: IEEE Conference on Communications (ICC), pp. 1–7 (2016)
Talha, A.M., Kamel, I., Aghbari, Z.A.: Enhancing confidentiality and privacy of outsourced spatial data, pp. 13–18 (2015)
Wang, B., Li, M., Wang, H., Li, H.: Circular range search on encrypted spatial data. In: IEEE Conference on Distributed Computing Systems (ICDCS), pp. 794–795 (2015)
Yi, X., Paulet, R., Bertino, E., Varadharajan, V.: Practical approximate k nearest neighbor queries with location and query privacy. IEEE Trans. Knowl. Data Eng. 28(6), 1546–1559 (2016)
Zhu, H., Liu, F., Li, H.: Efficient and privacy-preserving polygons spatial query framework for location-based services. IEEE Internet Things J. 4(2), 536–545 (2017)
Zhu, H., Lu, R., Huang, C., Chen, L., Li, H.: An efficient privacy-preserving location-based services query scheme in outsourced cloud. IEEE Trans. Veh. Technol. 65(9), 7729–7739 (2016)
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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DOI: https://doi.org/10.1007/978-3-030-05063-4_12
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