Framework of geofence service using dummy location privacy preservation in vehicular cloud network

Hani Al-Balasmeh, Maninder Singh, Raman Singh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
24 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)66-77
Number of pages12
JournalInternational Arab Journal of Information Technology
Volume20
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • geofence service
  • location privacy preservation
  • user-authentication
  • vehicular cloud

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