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The rise of the Internet of Vehicles has brought revolutionary changes to the transportation system and also presented new challenges for data privacy protection. This study is dedicated to exploring how advanced machine learning techniques can enhance the data processing capabilities of the Internet of Vehicles while ensuring the security and privacy of communication data. The research emphasizes the collaborative role of federated learning and deep reinforcement learning. The introduction of federated learning facilitates collaborative learning of distributed data without the need to share raw information, a significant advantage in the decentralized environment of the Internet of Vehicles. Additionally, the application of deep reinforcement learning in the decision-making process provides efficient adaptability to various complex scenarios in the Internet of Vehicles environment. In terms of differential privacy protection, the study discusses protective strategies based on a layered correlated propagation mechanism aimed at reducing the risk of individual information leakage. Furthermore, reinforcement learning plays a crucial role in constructing defense mechanisms, enhancing the system's resistance against potential privacy attacks. Through comprehensive experiments, the proposed methods in the paper can be effectively implemented in practical Internet of Vehicles environments, demonstrating significant effects. The experiments show that the differential privacy protection strategy combining federated learning and reinforcement learning successfully reduces the risk of privacy leakage without sacrificing data utility. This discovery not only holds important significance for academic research but also provides practical solutions for ensuring the data security of Internet of Vehicles in practice.
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