Security-Enhanced Spatial Range Query Over Large-Scale Encrypted Mobile Cloud Datasets

Privacy-preserving spatial range query allows users to obtain valid data based on specific spatial attributes or geographical location while ensuring privacy. However, many existing Privacy-Preserving Spatial Range Query (PSRQ) schemes generally face the problems of low query efficiency and insufficient security when dealing with large-scale mobile cloud data sets, and it is difficult to resist Indistinguishability under Chosen-Plaintext Attack (IND-CPA). To solve these challenges, we first propose an Efficient and Secure Spatial Range Query scheme (ESSRQ), which is based on a dual mobile cloud architecture by integrating Geohash algorithm, Circular Shift Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), achieving a constant search complexity. However, ESSRQ cannot protect the access patterns, where the cloud server still has the potential to infer attacks based on the index position and even obtain plaintext queries. On this basis, we further propose an extended scheme ESSRQ-PIR, which introduces Private Information Retrieval (PIR) into single mobile cloud-based architecture, effectively prevents the leakage of access patterns, enhances the security of ESSRQ and can also realize efficient query on large-scale cloud datasets. Formal security analysis proves that our proposed schemes are secure against IND-CPA, and extensive experiments demonstrate that our schemes improve the query efficiency by up to nearly 20 times when compared with previous solutions. These features make the proposed schemes particularly suitable for privacy-preserving spatial queries in mobile cloud computing environments.