A Novel Two Level Search Scheme to Provide Security and Privacy of Encrypted Spatial Data
Keywords:
Geometric range queries, Spatial Data, Encrypted dataAbstract
Spatial data consists of geographic and geometric data primitives. Searching on spatial data is carried by geometric range queries. FastGeo, an efficient two-level search scheme is introduced. The major contributions focus on two aspects. First, enrich search functionalities by designing new solutions to carry out fundamental geometric search queries, which are supported over encrypted data. Second, minimize the gap between theory and practice by building novel schemes to perform geometric queries with highly efficient search time and updates over large scale spatial data. Spatial data and geometric range queries are converted into a new form, denoted as equality-vector form and perform two-level search to verify whether a point is inside a geometric range. FastGeo is implemented using java as programming language on Net Beans IDE. An honest-but-curious server efficiently performs geometric range queries and returns data points that are inside the geometric region to the client without learning query or sensitive information. Better privacy can be achieved which cannot drop or create a new message. Experimental results on spatial data can achieve sublinear search time.
References
Boyang Wang, Ming Li, and Li Xiong, “FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data” IEEE Transactions on Dependable and Secure Computing, 2017.
B. Wang, M. Li, and H. Wang, “Geometric Range Search on Encrypted Spatial Data”, IEEE Transactions on Information Forensics and Security, vol. 11, no. 4, pp. 704–719, 2016.
Shashi Shekhar, Stenven k. Feiner, and Walid G. Aref, “Spatial Computing,” Communications of the ACM, 2016.
Erik-Oliver Blass, Travis Mayberry, and Guevara Noubir, “Practical Forward Secure Range and Sort Queries with Updated-Oblivious Linked Lists”, Proceedings of Privacy Enhancing Technology Symposium, 2015.
Florian Kerschbaum, “Frequency-Hiding Order-Preserving Encryption”, Proceedings of ACM, 2015.
B. Wang, M. Li, H. Wang, and H. Li, “Circular Range Search on Encrypted Spatial Data”, Proceedings of IEEE CNS, 2015.
H. Zhu, R. Lu, C. Huang, L. Chen, and H. Li, “An Efficient Privacy Preserving Location Based Services Query Scheme in Outsourced Cloud,” IEEE Transactions on Vehicular Technology, 2015.
V. Pappas, F. Krell, B. Vo, V. Kolesnikov, T. Malkin, S. G. Choi, W. George, A. Keromytis, and S. Bellovin, “Blind Seer: A Searchable Private DBMS,” Proceedings of IEEE (S&P), 2014.
J. Sedenka and P. Gasti, “Privacy-Preserving Distance Computation and Proximity Testing on Earth, Done Right,” Proceedings of ACM ASIA Conference on Information, Computer and Communications Security (ASIACCS), 2014.
B. Wang, Y. Hou, M. Li, H. Wang, and H. Li, “Maple: Scalable Multi-Dimensional Range Search over Encrypted Cloud Data with Tree-based Index,” Proceedings of ACM, 2014.
G. Ghinita and R. Rughinis, “An Efficient Privacy-Preserving System for Monitoring Mobile Users: Making Searchable Encryption Practical,” Proceedings of ACM Conference on Data and Application Security and Privacy (CODASPY), 2014.
E. Shen, E. Shi, and B. Waters, “Predicate Privacy in Encryption Systems,” Proceedings of Transactions on cloud computing (TCC), 2009.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
