Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

Abstract

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatio-textual data is outsourced to the cloud server to reduce the local high storage and computing burdens, but at the same time causes security issues such as data privacy leakage. Thus, extensive privacy-preserving spatial keyword query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) for encryption, but ASPE has proven to be insecure. And the existing spatial range query schemes require users to provide more information about the query range and generate a large amount of ciphertext, which causes high storage and computational burdens. To solve these issues, in this paper we introduce some random numbers and a random permutation to enhance the security of ASPE scheme, and then propose a novel privacy-preserving Spatial Keyword Query (SKQ) scheme based on the enhanced ASPE and Geohash algorithm. In addition, we design a more Lightweight Spatial Keyword Query (LSKQ) scheme by using a unified index for spatial range and multiple keywords, which not only greatly decreases SKQ’s storage and computational costs but also requires users to provide little information about query region. Finally, formal security analysis proves that our schemes have Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our enhanced scheme is efficient and practical.

Keywords

Cloud servers, Keyword queries, Large amounts, Outsourcing data, Privacy preserving, Query schemes, Scalar product, Spatial keyword query, Spatio-textual data, Textual data

Discipline

Computational Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems, Bologna, Italy, 2022 July 10 - 13

First Page

392

Last Page

402

ISBN

9781665471770

Identifier

10.1109/ICDCS54860.2022.00045

City or Country

Bologna

Additional URL

https://doi.ieeecomputersociety.org/10.1109/ICDCS54860.2022.00045

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