Hilbert curves-based location privacy technique for vehicular cloud networks

Location privacy (LP) plays an important role in location-based services (LBS) offered by different service providers. Many applications request users to enable the location services in their smart devices and access geodata from their smartphones which becomes a security threat. To enhance the individual’s privacy while accessing LBS systems, this paper proposed a novel Hilbert curve (HC) based algorithm for generating k-dummy anonymous locations. These anonymous location helps to generate anonymous trajectories when a user requests a point of interest (POI) from the LBS in a vehicular cloud network (VCN). The proposed technique can generate a dummy block to fill it with the k-dummy location of each request from the user. The proposed approach consists of the following four stages. The first one starts with registering the different smart internet of things devices (IoT) in the VCN. The second one includes processing the devices’ trajectories into VCN. The third one generates the k-dummy location of trajectories in LP and applies the HC to fill the square spaces. The Fourth stage consists in requesting anonymous POI from the LBS. The results show the proposed approach’s effectiveness on parameters like flexibility and reliability of filling the square space in different iterations.