Privacy-Preserving Top-k Spatial Keyword Queries in Fog-Based Cloud Computing

Abstract

With the popularity of location based services, spatial keyword query has become an important application. In order to save the storage and computational costs, most data owners will outsource the data to the cloud server, but this will lead to two problems, such as privacy leakage and heavy network bandwidth burden. To solve above problems, we propose a Privacy-preserving top-k Spatial Keyword queries based on Fog computing, namly PSKF. To further improve the search efficiency, we use the IR-tree to build the index and store it in the cloud server. Each fog server also saves a different subtree of the IR-tree, so that we can decide which fog server to participate in the query by pruning. Formal security analysis shows that our proposed PSKF achieves Indistinguishability under Known-Plaintext Attacks (IND-KPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible in practical applications.