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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Privacy-Preserving Top-k Spatial Keyword Queries over Outsourced Database
verfasst von : Sen Su, Yiping Teng, Xiang Cheng, Yulong Wang, Guoliang Li
Erschienen in: Database Systems for Advanced Applications
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In this paper, we study the privacy-preserving top-
$$k$$
spatial keyword query problem in outsourced environments. Existing studies primarily focus on the design of privacy-preserving schemes for either spatial or keyword queries, and they cannot be applied to solve the privacy-preserving spatial keyword query problem. To address this problem, we present a novel privacy-preserving top-
$$k$$
spatial keyword query scheme. In particular, we build an encrypted tree index to facilitate privacy-preserving top-
$$k$$
spatial keyword queries, where spatial and textual data are encrypted in a unified way. To search with the encrypted tree index, we propose two effective techniques for the similarity computations between queries and tree nodes under encryption. Thorough analysis shows the validity and security of our scheme. Extensive experimental results on real datasets demonstrate our scheme achieves high efficiency and good scalability.