Efficient and secure Spatial Range Query over large-scale encrypted data

• Main Authors: MIAO, Yinbin, XU, Chao, ZHENG, Yifeng, LIU, Ximeng, MENG, Xiangdong, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Privacy; Query processing; Distributed databases; Computer architecture; Filtering algorithms; Spatial databases; Cryptograph; Spatial range query; Information Security; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8559
• Institution: Singapore Management University

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.