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Title: A Framework to Enhance the Location Privacy using Geo-fence Service over Vehicular Cloud Network
Authors: Al-Balasmeh, Hani Ibraheem Mohammed
Supervisor: Singh, Maninder
Singh, Raman
Keywords: Vehicular Cloud Network;Privacy Preservation;Geofence service;Location-Based Services;Dummy Location;Security;Token Management;Authentication;Data and Location Privacy;Applied Cryptography;Data and obfuscation Privacy K-anonymity;Hilbert Curve;Internet of Things
Issue Date: 16-Feb-2023
Abstract: The leakage of sensitive data of users and Global Positioning System (GPS) location trajectories from different cloud computation sources has increased in daily life. This critical problem can influence users’ lives because attackers try to misuse the leaked information. Many applications require users to enable the location service on their devices. In this research, we proposed techniques to solve three major critical problems define as, location privacy, data privacy, and authentication privacy. These three problems are addressed in four different parts of the research. In the first part, we proposed a technique to solve the existing problem of location privacy in the geofence service. A TIET-GEO framework is proposed based on ge-ofence service to define the geo-boundary virtual area of the internet of things (IoT) GPS devices. The procedures involved in the framework are divided into two parts: First, to define, monitor, and view several types of geofence areas and trigger any action of entering or exiting a particular area by notifying the users or trusted third party. The second part, emphasizes providing k-dummy location privacy of user’s trajectories when the users request a point of interest (POI) from the location-based services (LBS) in the geofence area. In the second part, we addressed the critical problem of the privacy of personal identification information (PII) and location information over vehicular cloud networks (VCNs). We propose a data and location privacy (DLP) framework that preserves data privacy in terms of the anonymity of PII and provides location privacy for users’ trajectories through the obfuscation technique. and provision of cryptographic security of the service communication. In the third part, to improve the security of mobile users’ authentication and the security of their devices, authentication in the VCNs and its services. Our proposed authentication framework” TIETA” is expected to enhance the security and validity of the user’s access to the services. The name of the proposed framework (TIETA) is inspired by our parent institution name (TIET) and is so named as TIET Authentication (TIETA) framework. The proposed framework is based on three factors of authentication; Single Factor Authentication (SFA) by checking users’ credentials details using JSON Web Token (JWT). Second Factor Authentication (2FA), checks users’ PIN code and Time-based one-time password (TOTP) to verify their identity. Multiple Factor Authentication (MFA), The RSA technique enables MFA based on predicting the location of the requesting device, and secure communication between a user and the server. In the fourth part, to enhance the privacy of the individual devices while accessing LBS systems, we 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 POI from the LBS in the VCN. The proposed technique can generate a dummy block to fill it with the k-dummy location of each request from the user by requesting anonymous POI from the LBS. The results obtained show the effectiveness of the proposed approaches and the framework of the research methodology. The proposed methodologies are evaluated using parameters like flexibility and reliability of applying the privacy techniques and enhancing the authentication parts of the user accessing services along with secure communication between the user and VCN services. The accuracy of the proposed TIET-GEO framework is found to be 93% which means the majority of the time device correctly identified the location of the IoT device when entering the geofence area. The precision of the proposed framework is achieved as 95%. Furthermore, based on achieved results during the testing of the data privacy preservation, the latency of the DLP framework for the process of anonymizing and re-anonymizing the data for PII is estimated to be 30000ms. In addition, the latency which is the time by the RSA-based encryption system to encrypt packages of PII data correctly is found to be 750000ms. Moreover, the latency time of the TIETA framework which is the time taken by the RSA-based encryption system to correctly encrypt messages containing OTP data between the client and server is 15 ms. For the HC technique, the latency time of the HC-dummy location to generate a K-dummy location trajectory of data is 750 ms when tested for different K values like 6, 7, and 8.
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