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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 8109730, 15 pages
Research Article

Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud

1School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
2School of Software, North University of China, Taiyuan, Shanxi 030051, China
3Department of Computer Science and Technology, Dalian Neusoft University, Dalian, Liaoning 116023, China

Correspondence should be addressed to Jun Hong

Received 20 May 2017; Revised 1 October 2017; Accepted 5 November 2017; Published 13 December 2017

Academic Editor: M. L. R. Varela

Copyright © 2017 Jun Hong 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.


As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP) to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate -nearest neighbors (kNN) query and query authentication on an encrypted outsourced spatial database. An asymmetric scalar-product-preserving encryption scheme is elucidated, in which data points and query points are encrypted with diverse encryption keys, and the CSP can determine the distance relation between encrypted data points and query points. Furthermore, the similarity search tree is extended to build a novel verifiable SS-tree that supports efficient query and query verification. It is indicated from the security analysis and experiment results that our scheme not only maintains the confidentiality of outsourced confidential data and query points but also has a lower query processing and verification overhead than the MR-tree.