电讯技术2025，Vol.65Issue(7)：1007-1015,9.DOI:10.20079/j.issn.1001-893x.250113002
面向VANETs身份持续认证的隐私保护联邦学习方案
Privacy-preserving Federated Learning for Continuous Authentication Scheme in VANETs
摘要
Abstract
For the issues of privacy leakage and identity authentication deficiency in federated learning(FL)for vehicular ad-hoc networks(VANETs),a privacy-preserving federated learning for continuous authentication(PPFLCA)scheme in VANETs is proposed.The scheme updates global models via a three-tiered architecture comprising vehicles,road side units(RSUs),and an aggregation center.Specifically,PPFLCA initiates vehicle and RSU registration while generating pseudonyms for vehicles.Subsequently,a key agreement protocol is employed to generate shared keys,which is combined with a pseudorandom generator(PRG)to produce random masks,and the scheme further adopts 16-bit fixed-point numbers to encrypt and optimize local gradients.Finally,PPFLCA continuously authenticates the identities of vehicles and RSUs,as well as their local gradients in each iteration.Security analysis demonstrates that PPFLCA effectively fulfills privacy preservation requirements while resisting fundamental attacks and replay attacks.Experimental evaluations reveal classification accuracies of 94.8%and 44.4%on MNIST and CIFAR-10 datasets respectively(with error margin less than 0.5%compared with plaintext training).With gradient parameters of 5×103,the scheme achieves 18 ms encryption latency per vehicle and maintains a low communication overhead of 0.367 KB per vehicle.关键词
车辆自组织网络(VANETs)/联邦学习/隐私保护/身份认证Key words
vehicular ad-hoc networks(VANETs)/federated learning/privacy preserving/identity authentication分类
信息技术与安全科学引用本文复制引用
夏元俊,黎利辉,任舒扬,余乐乐,刘忆宁..面向VANETs身份持续认证的隐私保护联邦学习方案[J].电讯技术,2025,65(7):1007-1015,9.基金项目
国家自然科学基金资助项目(62072133) （62072133）
温州市重大科技创新攻关项目(ZG2023028) （ZG2023028）
