Efficient and secure Spatial Range Query over large-scale encrypted data
Publication Type
Conference Proceeding Article
Publication Date
7-2023
Abstract
Spatial range query enjoys widespread application scenarios due to the ever-growing geo-positioning technology in recent years. Huge amounts of encrypted geo-location data are being outsourced to cloud servers to alleviate local storage and computational overheads without leaking sensitive information. However, most existing Privacy-preserving Spatial Range Query (PSRQ) cannot achieve high efficiency while satisfying strong security over large-scale encrypted spatial data. To strike a best possible balance between security and efficiency, we propose a novel efficient Privacy-preserving Spatial Range Query (eP-SRQ) scheme in dual-cloud architecture over large-scale dataset. Specifically, we propose an efficient PSRQ scheme by designing a novel index structure based on Geohash algorithm, Circular Shift and Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), which makes the computational complexity of query process independent of dataset size. Formal security analysis proves that our scheme can achieve Indistinguishability against Chosen-Plaintext Attack (IND-CPA), and extensive experiments prove that our scheme is feasible in real-world applications.
Keywords
Privacy, Query processing, Distributed databases, Computer architecture, Filtering algorithms, Spatial databases, Cryptograph, Spatial range query
Discipline
Information Security | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization; Cybersecurity
Publication
Proceedings of the 43rd IEEE International Conference on Distributed Computing Systems (ICDCS 2023), Hong Kong, China, July 18-21
First Page
271
Last Page
281
ISBN
9798350339871
Identifier
10.1109/ICDCS57875.2023.00055
Publisher
IEEE
City or Country
New York, NY, USA
Citation
MIAO, Yinbin; XU, Chao; ZHENG, Yifeng; LIU, Ximeng; MENG, Xiangdong; and DENG, Robert H..
Efficient and secure Spatial Range Query over large-scale encrypted data. (2023). Proceedings of the 43rd IEEE International Conference on Distributed Computing Systems (ICDCS 2023), Hong Kong, China, July 18-21. 271-281.
Available at: https://ink.library.smu.edu.sg/sis_research/8559
Additional URL
https://doi.org/10.1109/ICDCS57875.2023.00055