标题： 一种结合联邦学习和差分隐私的广告个性化隐私保护框架 显示英文标题

标题： A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy

摘要： 为缓解个性化广告中的隐私泄露和性能问题，本文提出了一种集成联邦学习和差分隐私的框架。 该系统结合分布式特征提取、动态隐私预算分配和鲁棒模型聚合，以平衡模型精度、通信开销和隐私保护。 多方安全计算和异常检测机制进一步增强了系统对恶意攻击的抗性。 实验结果表明，该框架在确保隐私的前提下实现了推荐准确性和系统效率的双重优化，为将隐私保护技术应用于广告推荐提供了实用解决方案和理论基础。 显示英文摘要

摘要： 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.