High-Dimensional and Secure Spatial Keyword Query With Arbitrary Ranges in Mobile Cloud

作者：Fuyuan Song, Yunlong Gao, Mingyang Zhao, Chuan Zhang, Zheng Qin, Bin Xiao · 发表于：IEEE Transactions on Mobile Computing · 年份：2025 · DOI：10.1109/tmc.2025.3581562 · 被引用次数：9 · 研究领域：Caching and Content Delivery、Data Management and Algorithms、Peer-to-Peer Network Technologies

Spatial keyword query has emerged as a critical service in mobile cloud, enabling cloud servers to retrieve spatiotextual objects within a mobile user's query range that contain specified query keywords. Numerous secure spatial keyword query schemes have been developed to enable geometric range queries and keyword searches on encrypted spatial data. However, spatial keyword queries are typically designed for searching high-dimensional spatial data across arbitrary geographic ranges. Most of them fail to handle arbitrary geometric range queries and efficient spatial keyword query over high-dimensional encrypted data. To address these issues, we propose a high-dimEnsional and Privacy-preserving Spatial Keyword Query (EPSKQ) scheme with arbitrary geometric ranges over encrypted spatial data, leveraging Hilbert curve encoding and Enhanced Matrix-based Inner Product Encryption (EMIPE). In EPSKQ, spatial locations and multi-keywords are encoded into compact vectors, and arbitrary geometric range queries are transformed into range intersection tests. To reduce computational overhead, we employ vector bucketing technique to partition large-size vectors into several small-size sub-vectors. Furthermore, we design a novel index structure called Hilbert Binary tree (HB-tree) to optimize range intersection tests. Based on HB-tree, we propose an enhanced spatial keyword query scheme, named EPSKQ+, which further improves query performance. Security analysis demonstrates that both EPSKQ and ...