结合联邦学习和增强学习的车联网数据差分隐私保护

Differential Privacy Data Protection for Internet of Vehicle by Combining Federated Learning and Reinforced Learning

为保证车联网环境下用户数据的安全性和隐私性,提出了结合联邦学习和增强学习的分布式数据差分隐私保护方案。利用联邦学习架构将数据保留在车辆节点或边缘设备上进行学习,通过分布式存储实现数据隐私保护,并减少数据传输开销;基于拉普拉斯机制实现差分隐私,并通过逐层相关传播(LRP)技术管理数据扰动,确保模型参数传递的隐私性和高效率。试验结果表明,所提出的方案在10轮通信内实现了约80%的全局准确度,最高可达98%,能够在消耗较少通信轮数的情况下完成模型聚合,实现了隐私保护和全局数据准确度的较好平衡,且通过增强学习策略准确检测到虚假噪声的注入,能够提升车联网的智能化水平和安全等级。

To ensure the security and privacy of sensitive data in Internet of Vehicle(IoV)environments,this paper proposed a distributed differential privacy data protection scheme combining federated learning and reinforced learning mechanisms.In this scheme,a federated learning architecture was applied to keep data on vehicle nodes or edge devices for learning,enabling data privacy protection,reducing data transmission costs through distributed storage.The Laplace mechanism was employed to achieve differential privacy,the Layer-wise Relevance Propagation(LRP)was used to manage data perturbation,ensuring the privacy and efficiency of model parameter transmissions.Experimental results show that the proposed scheme can achieve approximately 80%global accuracy within 10 rounds of communication,with a maximum of 98%,can complete model aggregation within less communication rounds,achieving a good balance between privacy protection and global data accuracy,and accurately detecting the injection of false noise through the reinforced learning strategy,promoting the intelligence and security levels of IoV.