A Comparison of Privacy Enhancing Technologies in Internet of Vehicle Systems

Abstract

The evolution of internet of things (IoT) is driving conventional vehicle ad-hoc networks into the internet of vehicles (IoV). IoV has shown great relevance in helping to improve traffic efficiency by easing traffic congestion. Vehicles can easily exchange information with other vehicles and infrastructure in the same environment as they are. However, the exchange of information in IoV introduces privacy challenges to vehicle owners. Private data of these IoV users are being leaked unintentionally to the system, some systems even send user’s data to a third party system and most times the user’s are not aware of such message exchange with their private data. It is possible for a honest but curious IoV user to identify vehicles and track user’s location by analysing messages exchanged between systems. Messages exchanged between these systems carry some form of vehicular identification and in turn can be used to trace an owner via location profiling. This thesis follows a structured approach towards mitigating this privacy leakage by applying the privacy enhancing technologies (PETs) identified in this thesis paper (encryption and attribute based credential), to help in protecting the information exchanged between each IoV’s involved in communication. This approach is beneficial to system entities compliance in privacy frameworks and also helps in identifying the most effective PET by comparing the state and condition of each data objects identified in the system.