Trace Your Footprint: Efficient Spatial Keyword Query Over Encrypted Trajectory Data

With the popularity of mobile devices, spatial-textual trajectory query has been deployed in applications such as trajectory-based navigation and travel route recommendation. Massive trajectory data have been outsourced to cloud servers for storage and sharing such as spatial keyword search. However...

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Vydáno v:IEEE transactions on information forensics and security Ročník 20; s. 11936 - 11949
Hlavní autoři: Miao, Yinbin, Wang, Xin, Zhang, Shu, Li, Xinghua, Xu, Shujiang, Liu, Zhiquan, Raymond Choo, Kim-Kwang, Deng, Robert H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Accuracy
Costs
Electronic mail
Encryption
Filtering algorithms
filtering-verification mechanism
Fitting
Indexes
Polynomials
Spatial databases
spatial keyword query
spatial-textual index structure
Trajectory
trajectory compression
Trajectory query
ISSN:1556-6013, 1556-6021
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Shrnutí:With the popularity of mobile devices, spatial-textual trajectory query has been deployed in applications such as trajectory-based navigation and travel route recommendation. Massive trajectory data have been outsourced to cloud servers for storage and sharing such as spatial keyword search. However, existing solutions only support similarity queries in the spatial dimension and still incur high storage and query costs, which cannot scale well in large-scale trajectory data scenarios. To solve the above issues, we first achieve an Efficient Range Query over Encrypted Trajectory Data (<inline-formula> <tex-math notation="LaTeX">\textsf {ERT} </tex-math></inline-formula>) using Douglas-Peucker trajectory compression algorithm, random matrix multiplication, filtering-verification mechanism and polynomial fitting technology. Then, we further propose an enhanced Efficient Spatial Keyword Query over Encrypted Trajectory Data (<inline-formula> <tex-math notation="LaTeX">\textsf {ESKT} </tex-math></inline-formula>) by constructing a unified spatial-textual index structure, which can find relevant trajectories that are within some arbitrary geometric range and contain all query keywords. Finally, we formally prove that our schemes are secure against chosen-plaintext-attack, and conduct extensive experiments to demonstrate that our schemes improve the query efficiency by almost <inline-formula> <tex-math notation="LaTeX">100\times </tex-math></inline-formula> when compared with state-of-the-art solutions.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2025.3624950