Framework of Geofence Service using Dummy Location Privacy Preservation in Vehicular Cloud Network
Hani Al-Balasmeh Computer Science and Engineering Department, Thapar This email address is being protected from spambots. You need JavaScript enabled to view it. |
Maninder Singh Computer Science and Engineering Department, Thapar This email address is being protected from spambots. You need JavaScript enabled to view it. |
Raman Singh School of Computing, Engineering, and Physical Sciences, This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: With the increasing prevalence of different mobile apps, many applications require users to enable the location service on their devices. For example, the geofence service can be defined as establishing virtual geographical boundaries. Enabling this service triggers entering and exiting the boundary area and notifies the users and trusted third parties. The foremost concern of using geofence is the privacy of location coordinates shared among different applications. In this paper, a framework called ‘TIET-GEO’ is proposed that allows users to define the geofence boundary; in addition, it monitors Global Positioning System (GPS) devices in real-time when they enter/exit a specific area. The proposed framework also proposes a dummy privacy preservation algorithm to generate K-dummy locations around the real trajectories when the user requests the Point Of Interest (POI) from the Location-Based Services (LBS). This article aims to enhance the location privacy preservation in geofence service, by generating a k-dummy location around the user location based on the radius size of the geofence area. The proposed framework uses token keys authentication to authorize the users in the Vehicular Cloud Network (VCN) service by generating secret token keys authentication between the client and services. The results obtained show the effectiveness of the proposed framework was on parameters like flexibility and reliability of responses from different sources, such as smart IoT devices and datasets.
Keywords: Location privacy preservation, geofence service, vehicular cloud, user-authentication.
Received February 10, 2022; accepted April 13, 2022