用联邦学习与差分隐私保护广告推荐中的用户隐私。
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
- 联合多方数据训练模型，避免原始数据上传。
- 动态分配隐私预算，提升推荐准确率与效率。
- 适合关注隐私安全的广告平台与研究者。
为缓解个性化广告中的隐私泄露与性能问题，本文提出一种融合联邦学习与差分隐私的隐私保护框架。系统通过分布式特征提取、动态隐私预算分配与鲁棒模型聚合，在保证模型精度、降低通信开销的同时实现强隐私保护。引入多方安全计算与异常检测机制，增强系统对恶意攻击的防御能力。实验表明，该框架在推荐准确率与系统效率上实现双重优化，同时确保用户隐私，为广告推荐中隐私保护技术的应用提供了实用方案与理论基础。
原文摘要 · Abstract (English)
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.
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