The International Arab Journal of Information Technology (IAJIT)


Framework of Geofence Service using Dummy Location Privacy Preservation in Vehicular Cloud

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.

[1] Agarwal Y., Jain K., and Karabasoglu O., “Smart Vehicle Monitoring and Assistance Using Cloud Computing in Vehicular Ad Hoc Networks,” The International Journal of Transportation Science and Technology, vol. 7, no. 1, pp. 60-73, 2018.

[2] Al-Balasmeh H., Singh M., and Singh R., “Framework of Data Privacy Preservation and Location Obfuscation in Vehicular Cloud Networks,” Concurrency and Computation: Practice and Experience, vol. 34, no. 5, p. e6682, 2022.

[3] Benarous L., Bitam S., and Mellouk A., “CSLPPS: Concerted Silence-Based Location Privacy Preserving Scheme for Internet of Vehicles,” IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 7153-7160, 2021.

[4] Benarous L. and Kadri B., “Obfuscation-based Location Privacy-preserving Scheme in Cloud- enabled Internet of Vehicles,” Peer-to-Peer Networking and Applications, vol. 15, no. 1, pp. 461-472, 2022.

[5] Cheng G., Guo Y., Chen Y., and Qin Y., “Designating City-wide Collaborative Geofence Sites for Renting and Returning Dock-less Shared Bikes,” IEEE Access, vol. 7, pp. 35596-35605, 2019.

[6] Cho S. and Kim H., “Privacy Preserving Authenticated Key Agreement Based on Bilinear Pairing for Uhealthcare,” The International Arab Journal of Information Technology, vol. 18, no. 4, pp. 523-530, 2021.

[7] Durst C., Hacker J., and Berthelmann T., Digital Customer Experience, Springer, 2019.

[8] Elazab M., Noureldin A., and Hassanein H., “Integrated Cooperative Localization for Vehicular Networks with Partial GPS Access in Urban Canyons,” Vehicular Communications, vol. 9, pp. 242-253, 2017.

[9] Garzon S., Arbuzin D., and Küpper A., “Geofence Index: A Performance Estimator for the Reliability of Proactive Location-based Services,” in Proceeding of the 18th IEEE International Conference on Mobile Data Management, Daejeon, pp. 1-10, 2017.

[10] Greenwald A., Hampel G., Phadke C., and Poosala V., “An Economically Viable Solution to Geofencing for Mass-Market Applications,” Bell Labs Technical Journal, vol. 16, no. 2, pp. 21-38, 2011.

[11] Guo C., Guo W., Cao G., and Dong H., “A Lane- Level LBS System for Vehicle Network with High-precision BDS/GPS Positioning,” Computational Intelligence and Neuroscience, vol. 2015, 2015.

[12] Hara T., Suzuki A., Iwata M., Arase Y., and Xie X., “Dummy-Based User Location Anonymization under Real-World Constraints,” IEEE Access, vol. 4, pp. 673-687, 2016.

[13] Hayashida S., Amagata D., Hara T., and Xie X., “Dummy Generation Based on User-Movement Estimation for Location Privacy Protection,” IEEE Access, vol. 6, pp. 22958-22969, 2018.

[14] Liu J. and Wang S., “All-Dummy K-Anonymous Privacy Protection Algorithm Based on Location Offset,” Computing, pp.1-13, 2022.

[15] Lu H., Jensen C., and Yiu M., “Pad: Privacy-area Aware, Dummy-based Location Privacy in 76 The International Arab Journal of Information Technology, Vol. 20, No. 1, January 2023 Mobile Services,” in Proceedings of the 7th ACM International Workshop on Data Engineering for Wireless and Mobile Access, Vancouver Canada, pp. 16-23, 2008.

[16] Nait-Sidi-Moh A., Ait-Cheik-Bihi W., Bakhouya M., Gaber J., and Wack M., “On the Use of Location-Based Services and Geofencing Concepts for Safety and Road Transport Efficiency,” in Proceeding of the International Conference on Mobile Web and Information Systems, Paphos, pp. 135-144, 2013.

[17] Nardini G., Stea G., and Virdis A., “Geofenced Broadcasts via Centralized Scheduling of Device- to-Device Communications in LTE-Advanced,” in Proceeding of the Workshop on New Frontiers in Quantitative Methods in Informatics, Venice, pp. 3-17, 2017.

[18] Ni L., Yuan Y., Wang X., Yu J., and Zhang J., “A Privacy Preserving Algorithm Based on R- Constrained Dummy Trajectory in Mobile Social Network,” Procedia Computer Science, vol. 129, pp. 420-425, 2018.

[19] Niu B., Zhang Z., Li X., and Li H., “Privacy-Area Aware Dummy Generation Algorithms for Location-based Services,” in Proceeding of the IEEE International Conference on Communications, Sydney, pp. 957-962, 2014.

[20] Oliveira R., Cardoso I., Barbosa J., Da Costa C., and Prado M., “An Intelligent Model for Logistics Management Based on Geofencing Algorithms and RFID Technology,” Expert Systems with Applications, vol. 42, no. 15-16, pp. 6082-6097, 2015.

[21] Rodriguez Garzon S. and Deva B., “Geofencing 2.0: Taking Location-based Notifications to the Next Level,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, pp. 921-932, 2014.

[22] Skala V. and Majdisova Z., “Fast Algorithm for Finding Maximum Distance with Space Subdivision in E 2,” in Proceeding of the International Conference on Image and Graphics, Tianjin, pp. 261-274, 2015.

[23] “The PHP Framework for Web Artisans,” (n.d.), Laravel,, Last Visited, 2022.

[24] “UCI Machine Learning Repository: Bike Sharing Dataset Data Set,” (n.d.)). UCI Dataset Repository, Retrieved, ng+dataset, Last Visited, 2022.

[25] “UCI Machine Learning Repository: GPS Trajectories Data Set,” (n.d.). UCI Dataset Repository, Retrieved from ajectories, Last Visited, 2022.

[26] Ullah I., Shah M., Khan A., and Jeon G., “Privacy- preserving Multilevel Obfuscation Scheme for Vehicular Network,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 2, pp. e4204, 2021.

[27] Wang T., Zeng J., Bhuiyan M., Tian H., Cai Y., Chen Y., and Zhong B., “Trajectory Privacy Preservation Based on A Fog Structure for Cloud Location Services,” IEEE Access, vol. 5, pp. 7692- 7701, 2017.

[28] Wu D., Zhang Y., and Liu Y., “Dummy Location Selection Scheme for K-Anonymity in Location Based Services,” in Proceeding of the IEEE Trustcom/BigDataSE/ICESS, Sydney, pp. 441- 448, 2017.

[29] Wu X. and Sun G., “A Novel Dummy-based Mechanism to Protect Privacy on Trajectories,” in Proceeding of the IEEE International Conference on Data Mining Workshop, Shenzhen, pp. 1120- 1125, 2014.

[30] Wu Z., Li G., Shen S., Lian X., Chen E., and Xu G., “Constructing Dummy Query Sequences To Protect Location Privacy and Query Privacy in Location-Based Services,” World Wide Web, vol. 24, no. 1, pp. 25-49, 2021.

[31] Yim Y., Cho H., Kim S., Lee E., and Gerla M., “Vehicle Location Service Scheme Based on Road Map in Vehicular Sensor Networks,” Computer Networks, vol. 127, pp. 138-150, 2017.

[32] Yu H., Li G., Wu J., Ren X., and Cao J., “A Location-based Path Privacy Protection Scheme in Internet of Vehicles,” in Proceeding of the IEEE INFOCOM-IEEE Conference on Computer Communications Workshops, Toronto, pp. 665- 670, 2020.

[33] Zheng Y., Fu H., Xie X., Ma W., and Li Q., “Geolife GPS Trajectory Dataset-user Guide- Microsoft Research,” 2011.

[34] Zhou Z., Yu F., and Shang J., “iGeoNoti: A Fine- Grained Indoor Geo-notification System,” in Proceeding of the 4th International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services, Shanghai, pp. 192-196, 2016. Framework of Geofence Service using Dummy Location Privacy Preservation in ... 77 Hani Al-Balasmeh is a PhD student in the Computer Engineering Department at the Thapar Institute of Engineering & Technology in Patiala, India. He recently received his M.Sc. in Information Technology from the University of Mysore with (Distinction) in India. His research interest is in vehicular cloud networks, data & location privacy, and IoT. Maninder Singh is Ph.D. in Computer Science with specialization in Network Security and M.E. in Software Engineering from Thapar Institute of Engineering and Technology, Patiala and B.E. in Computer Engineering. He joined the Computer Science and Engineering Department of Thapar Institute of Engineering & Technology, Patiala (India) in 1996 and is presently serving as professor and dean of Academic Affairs. His research interests include Network Security, Cyber Physical Systems & Security and IoT. Raman Singh, is a Lecturer in the School of Computing, Engineering, and Physical Sciences, the University of the West of Scotland, Lanarkshire, United Kingdom. I have worked as a Postdoctoral Fellow at the School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Ireland from February 2020 to February 2021. I also worked as an Assistant Professor in the Computer Science and Engineering Department of Thapar Institute of Engineering and Technology, Patiala (India) from June 2016 to November 2021. My primary research interest is in the area of cyber and network security. I have completed a Post-Doc Fellowship in Blockchain and Applied Cryptography and a Ph.D. in Machine learning-enabled Intrusion Detection systems.