Abstract
Connected vehicles and smart cars have become highly reliant on location-based services (i.e. LBS) to provide accurate, personalized and intelligent services. However, location-based services have endangered its users to considerable risks concerning the privacy and security of users' personal data. Although existing research provides a myriad of methods to improve and protect user privacy in LBS applications, most of these methods are concerned with handling static queries and non-mobile objects only. Moreover, various issues and challenges still persist with regards to the need to trust third parties, overloading of the user, and low accuracy of the returned results. This paper contributes a Double Obfuscation Approach (referred to as DOA) that applies two phases of obfuscation consecutively whilst integrating two differing privacy protection approaches, namely Obfuscation and Trusted Third Party, and two techniques, namely fog caching technology and mix zone. In essence, the DOA obfuscates and hides the identity and location of its users using the fog nodes, which operate as a trusted third party (TTP), and without the need to reveal the identity of the users or trust the cooperating nodes. Moreover, this paper presents a DOA algorithm that improves the overall user privacy and system performance using the fog nodes, which split the responses of each query into five parts, thus reducing the processing time of the results by the user and enhancing the overall accuracy where the user directly selects the most suitable parts based on his current location. Overall, the hybrid DOA approach empowers the users of connected vehicle applications to protect their privacy through an algorithm that caters for the dynamic nature of user queries and mobility of objects. The results of our comparative simulations against well-known hybrid privacy protection methods demonstrate the superiority of the proposed Double Obfuscation Approach especially with respect to user privacy whilst maintaining a nominal overhead on the user, reduced response time and high accuracy of the obtained results.
Highlights
The Internet of Things (i.e. IoT) has changed the way people use services in all fields of life, such as health, transportation, business, education, energy, communication, entertainment, among others [1], [2]
EVALUATION METRICS AND SIMULATION RESULTS Our privacy protection approach is based on the principle of obfuscation but in a novel way
Before delving into the details of the simulation experiments, we identified a set of evaluation metrics that were used to guide the comparison of the Double Obfuscation Approach (DOA) with other privacy approaches
Summary
The Internet of Things (i.e. IoT) has changed the way people use services in all fields of life, such as health, transportation, business, education, energy, communication, entertainment, among others [1], [2]. Albouq et al.: DOA for Protecting the Privacy of IoT Location Based Applications communicate to each other or cooperate wirelessly to achieve a particular goal [13] Despite their benefits, smart objects have become a constant observer of users’ lives and may expose, intentionally or otherwise, their private data, which are stored in the cloud as a result of penetration by malicious entities. Many techniques have emerged in the area of privacy protection, such as the Dummy [30], K-Anonymity [17] and [31], TTP [31], Obfuscation [32], PIR [33], and Clacking Area [37] techniques These techniques still suffer from serious issues, such as the need to trust a third party, the weak accuracy of the results, the overload on the user, as well as the adverse effects on the performance of the whole system [19], [20]. Section seven highlights the findings of this research and suggests key implications for the privacy of mobile IoT objects
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