Shape-unconstrained privacy-preserving range query for fog computing supported vehicular networks using image

For Location-Based Service (LBS) in vehicular networks, there is a trend to leverage fog nodes to provide low-latency range query services. However, traditional coordinate-centric range query frameworks face performance and security challenges when they are deployed at fog nodes. First, traditional frameworks employ coordinate computation to search targets, thus they cannot support highly irregular areas at network edges. Second, existing privacy-preserving range query schemes based on the traditional framework only can protect search areas with certain shapes and bring high overhead to fog nodes. To address these challenges, we propose Image matching based Fine-grained range Query with Privacy-preserving for fog-enabled vehicular networks, named IFQP. Specifically, we firstly propose an image matching based fine-grained range query, which could search targets located at the edge based on the image. Moreover, to protect the privacy of the proposed image-centric range query framework, we develop a content-searchable image encryption algorithm. In particular, fog nodes can generate query responses based on images encrypted by the proposed algorithm. Detailed security analysis and experiments with real LBS datasets are done to demonstrate the security properties and efficiency of the proposed IFQP. To the best of our knowledge, this work is the first to introduce image technologies into range query field.