Abstract
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 k-nearest neighbors (kNN) query and kNN 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 kNN query and kNN 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 kNN query processing and verification overhead than the MR-tree.
Highlights
As the spatial data resources have been developed by leaps and bounds, to be well geared into such transition, the enterprises are required to proliferate the resources of both the hardware and software resources and to recruit professionals to manage and maintain data
We introduce an asymmetric scalar-product-preserving encryption to encrypt confidential data points and query points, and we propose an authenticated spatial index structure based on the similarity search tree (SS-tree) [17], called verifiable SS-tree (VSS-tree), for secure k-nearest neighbors (kNN) query processing and kNN query authentication
To make the reconstructed root hash hroot match sroot, verification object (VO) either comprises all the data entries in Ln or comprises the pair (C, hash) of Ln. For the former, the client can determine that p is one of a kNN query results according to the verification algorithm and there exists at least one point in the results whose distance to q is farther than that of p
Summary
As the spatial data resources have been developed by leaps and bounds, to be well geared into such transition, the enterprises are required to proliferate the resources of both the hardware and software resources and to recruit professionals to manage and maintain data. Recent studies [12,13,14,15,16] have proposed various techniques to support either kNN queries on encrypted data or kNN query authentication. Both privacy protection and query authentication should be provided in an insecure cloud computing environment. We introduce an asymmetric scalar-product-preserving encryption to encrypt confidential data points and query points, and we propose an authenticated spatial index structure based on the SS-tree [17], called verifiable SS-tree (VSS-tree), for secure kNN query processing and kNN query authentication.
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