Efficient Privacy-Preserving Spatial Data Query in Cloud Computing

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 s...

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Vydáno v:IEEE transactions on knowledge and data engineering Ročník 36; číslo 1; s. 122 - 136
Hlavní autoři: Miao, Yinbin, Yang, Yutao, Li, Xinghua, Wei, Linfeng, Liu, Zhiquan, Deng, Robert H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cloud computing
Cloud server
Cryptography
Cybersecurity
Data privacy
Encryption
Geographical locations
Indexes
Privacy
privacy-preserving
Queries
Query processing
query range
Security
security issues
Spatial data
Spatial databases
User requirements
ISSN:1041-4347, 1558-2191
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Shrnutí: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<inline-formula><tex-math notation="LaTeX">^+</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-3283020.gif"/> </inline-formula>) by using Geohash-based <inline-formula><tex-math notation="LaTeX">R</tex-math> <mml:math><mml:mi>R</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq2-3283020.gif"/> </inline-formula>-tree structure (called <inline-formula><tex-math notation="LaTeX">GR</tex-math> <mml:math><mml:mrow><mml:mi>G</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq3-3283020.gif"/> </inline-formula>-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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2023.3283020