Privacy-preserving ranked spatial keyword query in mobile cloud-assisted fog computing

• Main Authors: TONG, Qiuyun, MIAO, Yinbin LI, LIU, Ximeng, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Mobile cloud-assisted fog computing; spatio-textual data; privacy-preserving; ranked spatial keyword query; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/8653; https://ink.library.smu.edu.sg/context/sis_research/article/9656/viewcontent/PP_RankedSK_av.pdf
• Institution: Singapore Management University

With the increasing popularity of GPS-equipped mobile devices in cloud-assisted fog computing scenarios, massive spatio-textual data is generated and outsourced to cloud servers for storage and analysis. Existing privacy-preserving range query or ranked keyword search schemes does not support a unified index, and are just applicable for the symmetric environment where all users sharing the same secret key. To solve this issue, we propose a Privacy-preserving Ranked Spatial keyword Query in mobile cloud-assisted Fog computing (PRSQ-F). Specifically, we design a novel comparable product encoding strategy that combines both spatial and textual conditions tightly to retrieve the objects in query range and with the highest textual similarity. Then, we use a new conversion protocol and attribute-based encryption to support privacy-preserving retrieval and malicious user traceability in the asymmetric environment where different query users have different keys. Furthermore, we construct an R-tree-based index to achieve faster-than-linear retrieval. Our formal security analysis shows that data security can be guaranteed. Our empirical experiments using a real-world dataset demonstrate the efficiency and feasibility of PRSQ-F.