Abstract
In Intelligent Transportation Systems (ITS), ubiquitous connectivity and automation are essential components of the Internet of Vehicles (IoV). The real-time information provided by a data-driven ITS is based on extensive vehicular data. Cyberattacks and privacy breaches are inherent risks of the IoV, raising significant concerns about data security. As a solution, we propose a privacy-preserving and data dissemination model for managing vast amounts of data generated within the IoV. The model adapts to user-specific needs and is influenced by interactions with neighbours and service providers. ITS applications are studied to mitigate potential privacy risks by analyzing their requirements. A data-balancing approach is used to prevent privacy leaks among vehicular services by using data from different time intervals to validate operational parameters. Individual privacy is protected, and IoV infrastructure security and reliability are strengthened, both of which benefit neighbouring vehicles and the broader transportation network. “Integrating blockchain and deep learning provides a powerful approach to intrusion detection in Industrial IoT (IIoT) environments, combining blockchain's security and transparency with deep learning's precision in detecting threats while minimizing false positives. In Intelligent Transportation Systems (ITS), core elements of the Internet of Vehicles (IoV) rely on extensive vehicular data for real-time operations but face significant risks from cyberattacks and privacy breaches. To address these challenges, we propose a privacy-preserving data dissemination model tailored to user needs and influenced by interactions with neighbors and service providers. A data-balancing approach prevents privacy leaks by validating operational parameters using data from varying intervals. This model strengthens IoV security, safeguards individual privacy, and enhances the reliability of the transportation network.”
Published Version
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