When Geo-Text Meets Security: Privacy-Preserving Boolean Spatial Keyword Queries

In recent years, spatial keyword query has attracted wide-spread research attention due to the popularity of the location-based services. To efficiently support the online spatial keyword query processing, the data owners need to outsource their data and the query processing service to cloud platforms. However, the outsourcing services may raise privacy leaking issues because the cloud server on the platforms may not be trusted for both data owners and query users. Therefore, in this work, we first propose and formalize the problem of privacy-preserving boolean spatial keyword query under the widely accepted Known Background Thread Model. And then, we devise a novel privacy-preserving spatial-textual Bloom Filter encoding structure and an encrypted R-tree index. They can maintain both spatial and text information together in a secure way while answering the encrypted spatial keyword queries without the need for data decryption. To further accelerate the query processing, a compressed encrypted index is provided to deal with the challenges of the large dimension expansion and the expensive space consumption in the encrypted R-tree index. In addition, we develop the corresponding algorithms based on the designed index, and present the in-depth security analysis to show our work's satisfaction meeting the strong secure scheme. Finally, we demonstrate the performance of our proposed index and algorithms by conducting extensive experiments on four datasets under various system settings.

I. Introduction

In recent years, many researchers focus their sparked enthusiasm on spatial keyword query [1]–[8], [16], and massive commercial applications provide the corresponding services. For example, the Google Maps and Foursquare enable the spatial keyword queries to help users search their interested information in a specific region. A typical spatial keyword query is boolean spatial keyword queries [4]–[7], which takes both the spatial range condition and keyword condition into account. Fig. 1 presents an example to show a boolean spatial keyword query Q with the ‘red rectangle’ as the spatial condition , and ‘hospital’ as the keyword condition . Suppose there are eight objects O₁, ..., O₈ in the spatio-textual dataset D. Each object has the spatial coordinates (i.e., latitude and longitude) and a keyword set (e.g., text description). So O₇ will be returned as the result of the query as O₇ locates in and its keyword set covers altogether.