Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2023

Abstract

With the increasing popularity of GPS-equipped mobile devices in cloud-assisted fog computing scenarios, massive spatio-textual data is generated and outsourced to cloud servers for storage and analysis. Existing privacy-preserving range query or ranked keyword search schemes does not support a unified index, and are just applicable for the symmetric environment where all users sharing the same secret key. To solve this issue, we propose a Privacy-preserving Ranked Spatial keyword Query in mobile cloud-assisted Fog computing (PRSQ-F). Specifically, we design a novel comparable product encoding strategy that combines both spatial and textual conditions tightly to retrieve the objects in query range and with the highest textual similarity. Then, we use a new conversion protocol and attribute-based encryption to support privacy-preserving retrieval and malicious user traceability in the asymmetric environment where different query users have different keys. Furthermore, we construct an R-tree-based index to achieve faster-than-linear retrieval. Our formal security analysis shows that data security can be guaranteed. Our empirical experiments using a real-world dataset demonstrate the efficiency and feasibility of PRSQ-F.

Keywords

Mobile cloud-assisted fog computing, spatio-textual data, privacy-preserving, ranked spatial keyword query

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Mobile Computing

Volume

22

Issue

6

First Page

3604

Last Page

3618

ISSN

1536-1233

Identifier

10.1109/TMC.2021.3134711

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TMC.2021.3134711

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