A Support Vector Machine (SVM) Model for Privacy Recommending Data Processing Model (PRDPM) in Internet of Vehicles

Abstract

Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure. It aims to minimize privacy risks while ensuring service continuity and sustainability. The SVM model helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations. The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.