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

With the popularity of location based services, spatial keyword query has become an important application. In order to mininize storage and computational costs, most data owners will outsource the data to the cloud server. There are, however, implications such as potential for privacy leakage and network bandwidth overheads. To solve the above problems, we propose a Privacy-preserving top-k Spatial Keyword queries based on Fog computing, namely PSKF. To further improve search efficiency, we use 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.

Automated summary

With the rise in popularity of location-based services, spatial keyword queries have become an important application. To address the issues of privacy leakage and network bandwidth overheads, we propose PSKF, a Privacy-preserving top-k Spatial Keyword query system based on Fog computing. By utilizing IR-tree and distributing subtrees among fog servers, we achieve efficient search and improve search efficiency. Formal security analysis shows that our proposed PSKF scheme achieves Indistinguishability under Known-Plaintext Attacks (IND-KPA), and extensive experiments demonstrate its efficiency and feasibility in practical applications.