Construction of an AI-Driven Privacy Protection Framework for Traffic Big Data: A Perspective Based on the Fusion of Multimodal Image Recognition and Federated Learning
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Abstract
The rapid development of intelligent transportation systems has generated massive volumes of heterogeneous traffic data, creating significant challenges for privacy protection and secure data utilization. To overcome the limitations of centralized storage architectures and single privacy-preserving approaches, this study proposes an AI-driven privacy protection framework based on the integration of multimodal image recognition and federated learning. A hierarchical multimodal feature extraction architecture is first developed to process heterogeneous visual and traffic data efficiently. Federated learning is then employed to enable distributed collaborative model training while preventing raw data exposure. To further enhance privacy protection, differential privacy mechanisms and dynamic parameter aggregation strategies are incorporated to resist inference attacks and improve model robustness. Experimental analysis demonstrates that the proposed framework effectively balances privacy preservation and model performance while supporting large-scale distributed data processing. The proposed methodology provides a practical solution for secure intelligent transportation systems and offers valuable references for distributed sensing networks, wireless communication infrastructures, and secure information transmission environments.
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