面向车联网数据隐私保护的高效分布式模型共享策略

doi:10.11959/j.issn.1000-436x.2022074

Abstract

Abstract:

Aiming at the efficiency problem of privacy data sharing in the Internet of vehicles (IoV), an efficient distributed model sharing strategy based on blockchain was proposed.In response to the data sharing requirements among multiple entities and roles in the IoV, a master-slave chain architecture was built between vehicles, roadside units, and base stations to achieve secure sharing of distributed models.An asynchronous federated learning algorithm based on motivate mechanism was proposed to encourage vehicles and roadside units to participate in the optimization process.An improved DPoS consensus algorithm with hybrid PBFT was constructed to reduce communication costs and improve consensus efficiency.Experimental analysis shows that the proposed mechanism can improve the efficiency of data sharing and has certain scalability.

Key words: blockchain, IoV, federated learning, edge computing

CLC Number:

TP393

Zijia MO, Zhipeng GAO, Yang YANG, Yijing LIN, Shan SUN, Chen ZHAO. Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles[J]. Journal on Communications, 2022, 43(4): 83-94.

Figures/Tables 13

参数	含义
$v_{j}^{i}$	第j个路边单元范围内的第i个车辆
$r_{n}^{j}$	第n个基站范围内的第j个路边单元
$b_{n}$	主链中的第n个基站
$T_{v_{j}^{i}}^{cmp}$	车辆 $v_{j}^{i}$ 的本地计算时间
$w_{j}^{i} (t)$	第t次全局迭代中车辆 $v_{j}^{i}$ 训练后模型参数
F (w(t))	在主链层全局聚合的损失函数
${rate}_{v_{j}^{n}}$	车辆 $v_{j}^{i}$ 的通信传输速率
$T_{v_{j}^{i}}^{com}$	车辆 $v_{j}^{i}$ 的模型参数传输时间
T ^total	单次迭代中系统总体运行时间

References 18

[1]	YANG F C , WANG S G , LI J L ,et al. An overview of Internet of vehicles[J]. China Communications, 2014,11(10): 1-15.
[2]	CONTRERAS-CASTILLO J , ZEADALLY S , GUERRERO-IBA?EZ J A , . Internet of vehicles:architecture,protocols,and security[J]. IEEE Internet of Things Journal, 2018,5(5): 3701-3709.
[3]	BILOGREVIC I , JADLIWALA M , KALKAN K ,et al. Privacy in mobile computing for location-sharing-based services[C]// International Symposium on Privacy Enhancing Technologies Symposium. Berlin:Springer, 2011: 77-96.
[4]	LIANG X H , LI X , LUAN T H ,et al. Morality-driven data forwarding with privacy preservation in mobile social networks[J]. IEEE Transactions on Vehicular Technology, 2012,61(7): 3209-3222.
[5]	YING B D , MAKRAKIS D , HOU Z Z . Motivation for protecting selfish vehicles’ location privacy in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2015,64(12): 5631-5641.
[6]	WANG X F , HAN Y W , WANG C Y ,et al. In-edge AI:intelligentizing mobile edge computing,caching and communication by federated learning[J]. IEEE Network, 2019,33(5): 156-165.
[7]	MCMAHAN H B , MOORE E , RAMAGE D ,et al. Communication-efficient learning of deep networks from decentralized data[J]. arXiv Preprint,arXiv:1602.05629, 2016.
[8]	DAI H N , ZHENG Z B , ZHANG Y . Blockchain for Internet of things:a survey[J]. IEEE Internet of Things Journal, 2019,6(5): 8076-8094.
[9]	CHEN M , TIAN Y W , FORTINO G ,et al. Cognitive Internet of vehicles[J]. Computer Communications, 2018,120: 58-70.
[10]	ELBIR A M , SONER B , COLERI S . Federated learning in vehicular networks[J]. arXiv Preprint,arXiv:2006.01412, 2020.
[11]	ZHAO Y , ZHAO J , YANG M M ,et al. Local differential privacy-based federated learning for Internet of things[J]. IEEE Internet of Things Journal, 2021,8(11): 8836-8853.
[12]	CHEN Y , SUN X Y , JIN Y C . Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,31(10): 4229-4238.
[13]	LU Y L , HUANG X H , ZHANG K ,et al. Blockchain empowered asynchronous federated learning for secure data sharing in Internet of vehicles[J]. IEEE Transactions on Vehicular Technology, 2020,69(4): 4298-4311.
[14]	CHAI H Y , LENG S P , CHEN Y J ,et al. A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in Internet of vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2021,22(7): 3975-3986.
[15]	POKHREL S R , CHOI J . Federated learning with blockchain for autonomous vehicles:analysis and design challenges[J]. IEEE Transactions on Communications, 2020,68(8): 4734-4746.
[16]	DENG L . The MNIST database of handwritten digit images for machine learning research[best of the web][J]. IEEE Signal Processing Magazine, 2012,29(6): 141-142.
[17]	LECUN Y , BOTTOU L , BENGIO Y ,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11): 2278-2324.
[18]	NETZER Y , WANG T , COATES A ,et al. Reading digits in natural images with unsupervised feature learning[C]// NIPS Workshop on Deep Learning and Unsupervised Feature Learning.[S.l.:s.n.], 2011: 1-9.

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方案	设计	优点	缺点
文献[8]	数据持有者各自在本地训练模型，基于中央服务器聚合用户训练参数更新全局模型，迭代更新	降低了数据泄露风险	对数据隐私保护力度较弱
文献[11]	将联邦学习与本地差分隐私相结合，为联邦学习本地输出梯度增加噪声扰动，并用两位编码降低通信成本	提高了数据共享过程中的安全性，同时降低了通信成本	存在单点故障问题
文献[12]	将神经网络分为浅层和深层，深层网络参数的更新频率低于浅层，引入时间加权聚合策略，提高中心模型的准确性和收敛性	提升了通信效率和收敛速度	存在单点故障问题
文献[13]	利用许可区块链和本地有向无环图组成的混合区块链架构以提高模型参数的安全性和可靠性	降低了数据泄露风险，同时避免了单点故障问题	忽略了参与训练的车辆用户存在懈怠问题，共享效率低
文献[14]	采用分层区块链和分层联邦学习框架，车辆和路旁单元通过维护专属区块链账本记录联邦学习模型	增强了数据共享过程中的安全性，并在一定程度上给予参与方激励	忽略了联邦学习参数共享造成的通信成本
文献[15]	依靠更新奖励方法，设计了一个包含区块链参数的数学框架，通过对端到端时延的严格分析量化来推导出最佳块到达率，从而最小化系统时延	提高了通信效率，压缩了系统运行时间	忽略了联邦学习同步聚合导致的时延问题

数据集	FedAVG		ASTW-FedAVG		AFL-MSC
数据集	通信轮数	准确率	通信轮数	准确率	通信轮数	准确率
1@MNIST1@MNIST	75	97.2%	61	97.7%	31	97.9%
2@MNIST2@MNIST	85	97.2%	70	98.1%	32	98.5%
3@MNIST3@MNIST	73	97.3%	70	97.9%	31	98.7%
1@SVHN1@SVHN	126	92.3%	98	94.6%	66	94.0%
2@SVHN2@SVHN	156	90.2%	107	94.1%	81	95.1%
3@SVHN3@SVHN	137	92.6%	105	93.1%	75	93.6%

Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles

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