A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
This provides a practical solution for privacy-preserving advertising personalization, though it appears incremental as it combines existing techniques.
The paper tackled privacy leakage and performance issues in personalized advertising by proposing a framework integrating federated learning and differential privacy, achieving dual optimization of recommendation accuracy and system efficiency while ensuring privacy.
To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extraction, dynamic privacy budget allocation, and robust model aggregation to balance model accuracy, communication overhead, and privacy protection. Multi-party secure computing and anomaly detection mechanisms further enhance system resilience against malicious attacks. Experimental results demonstrate that the framework achieves dual optimization of recommendation accuracy and system efficiency while ensuring privacy, providing both a practical solution and a theoretical foundation for applying privacy protection technologies in advertisement recommendation.