Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

Abstract

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ$+$+) by using Geohash-based $R$R-tree structure (called $GR$GR-tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.

Keywords

Cloud server, privacy-preserving, query range, security issues, spatial data

Discipline

Information Security | Theory and Algorithms

Research Areas

Cybersecurity

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

36

Issue

1

First Page

122

Last Page

136

ISSN

1041-4347

Identifier

10.1109/TKDE.2023.3283020

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2023.3283020

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