基于同态加密和群签名的可验证联邦学习方案  

A Verifiable Federated Learning Scheme Based on Homomorphic Encryption and Group Signature

作　　者：李亚红 李一婧 杨小东[3] 张源 牛淑芬[3] LI Yahong;LI Yijing;YANG Xiaodong;ZHANG Yuan;NIU Shufen(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730000,China;School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chendu 610054,China;School of Computer Science and Engineering,Northwestern Normal University,Lanzhou 730000,China)

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730000 [2]电子科技大学计算机与工程学院,成都610054 [3]西北师范大学计算机工程学院,兰州730000

出　　处：《电子与信息学报》2025年第3期758-768,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62461032);甘肃省科技计划(22JR5RA158,22JR5RA350);甘肃省高校教师创新基金(2023A-041,2023-Z D-234);兰州交通大学-天津大学联合创新基金(LH2024003)。

摘　　要：在车载网络(VANETs)中,联邦学习(FL)通过协同训练机器学习模型,实现了车辆间的数据隐私保护,并提高了整体模型的性能。然而,FL在VANETs中的应用仍面临诸多挑战,如模型泄露风险、训练结果验证困难以及高计算和通信成本等问题。针对这些问题,该文提出一种面向联邦学习的可验证隐私保护批量聚合方案。首先,该方案基于Boneh-Lynn-Shacham(BLS)动态短群聚合签名技术,保护了客户端与路边单元(RSU)交互过程中的数据完整性,确保全局梯度模型更新与共享过程的不可篡改性。当出现异常结果时,方案利用群签名的特性实现车辆的可追溯性。其次,结合改进的Cheon-Kim-Kim-Song(CKKS)线性同态哈希算法,对梯度聚合结果进行验证,确保在联邦学习的聚合过程中保持客户端梯度的机密性,并验证聚合结果的准确性,防止服务器篡改数据导致模型训练无效的问题。此外,该方案还支持车辆在部分掉线的情况下继续更新模型,保障系统的稳定性。实验结果表明,与现有方案相比,该方案在提升数据隐私安全性和结果的可验证性的同时,保证了较高效率。Objective In Vehicular Ad-hoc NETworks(VANETs),network instability and frequent vehicle mobility complicate data aggregation and expose it to potential attacks.Traditional Federated Learning(FL)approaches face challenges such as high computational and communication overheads,insufficient privacy protection,and difficulties in verifying aggregation results,which impact model training efficiency and stability.To address these issues,this study proposes a scheme that integrates the Boneh-Lynn-Shacham(BLS)dynamic short group signature with an enhanced Cheon-Kim-Kim-Song(CKKS)homomorphic encryption technique.This approach reduces computational and communication costs,ensures data privacy under chosen-plaintext attacks,and maintains system stability by allowing vehicles to disconnect after submitting encrypted data.The proposed framework enhances privacy,verifiability,anonymity,traceability,and robustness,providing a secure and reliable FL solution for VANETs.Methods A batch aggregation scheme is proposed,integrating an improved CKKS linearly homomorphic encryption algorithm with a BLS-based dynamic short group signature technique to address key challenges in applying FL within VANETs.The improved CKKS linearly homomorphic encryption algorithm mitigates privacy leakage risks in vehicle data and training models.Data security and training privacy are ensured by maintaining ciphertext indistinguishability under chosen-plaintext attacks,preventing attackers from inferring original data from ciphertext and protecting vehicle users’privacy.Linearly homomorphic hashing verifies aggregation result correctness while reducing computational load.This approach also allows vehicles to disconnect after submitting encrypted data,enhancing system robustness and stability.Consequently,model training continuity and reliability are maintained even in dynamic and unstable vehicular network conditions.The BLS-based dynamic short group signature technique simplifies group signature generation,improving aggregation efficiency and reducing

关 键 词：隐私保护 联邦学习 车载自组网 可验证聚合 群签名 

分 类 号：TN918[电子电信—通信与信息系统] TP309.7[电子电信—信息与通信工程]