2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | IFS-7.2 | ||
| Paper Title | Enabling Efficient and Expressive Spatial Keyword Queries on Encrypted Data | ||
| Authors | Xiangyu Wang, Jianfeng Ma, Xidian University, China; Ximeng Liu, Fuzhou University, China | ||
| Session | IFS-7: Information Hiding, Cryptography and Cybersecurity | ||
| Location | Gather.Town | ||
| Session Time: | Friday, 11 June, 11:30 - 12:15 | ||
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 | ||
| Presentation | Poster | ||
| Topic | Information Forensics and Security: [APC] Applied Cryptography | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Recently, spatial keyword query services have been widely deployed in real-life applications, such as location-based services and social networking. Several privacy-preserving spatial keyword queries solutions were proposed to guarantee data security and query privacy on outsourced data. However, those solutions are either based on broken cryptographic tools or support a single query type, and hence cannot meet the security and functionality requirements in practical applications. In this paper, we propose a \textbf{S}ecure \textbf{S}patial \textbf{K}eyword \textbf{Q}ueries (SSKQ) construction supporting expressive query types. Specifically, we present a secure index structure for \textit{spatial-textual} data based on the encrypted Quadtree and Bloom filter, which can prune the index tree dynamically and only reveal the files associated with a set of keywords. The security analysis and the experiments conducted on real-world datasets demonstrate the security and performance of our construction. | ||