EPSRQ: Efficient Privacy-Preserving Spatial-Keyword Range Query Processing in Cloud

With the rapid development of location-based services in the mobile Internet, a large amount of spatial-keyword data for range queries is outsourced to the cloud to alleviate local storage and computational burdens. However, directly outsourcing such data to the untrusted cloud could lead to potential privacy issues because of data abuse or breaches. To address this issue, we propose an efficient privacy-preserving spatial-keyword range query processing in cloud in this paper. First, a keyword, location and range vectorization model is proposed. By using the vectorization and vector encryption, the spatial-keyword information is encrypted for confidentiality preservation. On the basis of the vectorization model and vector encryption, an equal partition-based keyword-location inverted index (EPKI-index) is constructed, and then we introduce the baseline spatial-keyword range query scheme (EPSRQ) by adopting the EPKI-index. To improve query efficiency, a binary keyword-filtering tree index (BKFtree-index) is designed, and the corresponding optimized range query scheme (EPSRQ+) is proposed. In addition, the game simulation-based proof is presented to analyze the security of the proposed scheme. Experimental results demonstrate that the proposed scheme has better performance on the query efficiency and storage.