Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2023
Abstract
With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ + by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ + achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100× compared with previous state-of-the-art solutions.
Keywords
Location-based services, location privacy leakage, privacy-preserving, spatial range query
Discipline
Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Information Forensics and Security
Volume
18
First Page
3921
Last Page
3933
ISSN
1556-6013
Identifier
10.1109/TIFS.2023.3288453
Publisher
Institute of Electrical and Electronics Engineers
Citation
MIAO, Yinbin; YANG, Yutao; LI, Xinghua; LIU, Zhiquan; LI, Hongwei; CHOO, Kim-Kwang Raymond; and DENG, Robert H..
Efficient privacy-preserving spatial range query over outsourced encrypted data. (2023). IEEE Transactions on Information Forensics and Security. 18, 3921-3933.
Available at: https://ink.library.smu.edu.sg/sis_research/8618
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TIFS.2023.3288453