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Traffic Monitoring in Self-Organizing VANETs: A Privacy-Preserving Mechanism for Speed Collection and Analysis
IEEE Wireless Communications ( IF 12.9 ) Pub Date : 2019-12-20 , DOI: 10.1109/mwc.001.1900123
Liehuang Zhu , Chuan Zhang , Chang Xu , Xiaojiang Du , Nadra Guizani , Kashif Sharif

With the explosive growth of vehicles, traffic monitoring has garnered significant attention in recent years. Collecting vehicular speed is an effective way to monitor traffic conditions and help vehicles to find optimal routes. However, further progress may be impeded due to users' privacy concerns. In addition, traffic monitoring is more difficult in a self-organizing VANET, since there is no centralized entity to collect and analyze the speed information. In this article, we mainly focus on privacy-preserving traffic monitoring in self-organizing VANETs. To address the unique features and security requirements of VANETs, we incorporate the homomorphic encryption, data perturbation, and super-increasing sequence in the proposed novel solution to resolve the challenges of efficient and privacy-preserving traffic monitoring. Security analysis shows that not only can our solution preserve vehicles' identities, locations, and data privacy, but it is also effective in mitigating collusion attacks. Moreover, experimental results confirm the efficiency of our solution in terms of computation and communication costs. Last but not least, some interesting challenges along with potential solutions are discussed, aiming to attract more research in this emerging area.

中文翻译:

自组织VANET中的流量监控:一种用于速度收集和分析的隐私保护机制

随着车辆的爆炸性增长,交通监控近年来引起了极大的关注。收集车速是监控交通状况并帮助车辆找到最佳路线的有效方法。但是,由于用户的隐私问题,可能会阻止进一步的发展。另外,由于没有集中的实体来收集和分析速度信息,因此在自组织的VANET中,流量监视更加困难。在本文中,我们主要关注自组织VANET中的隐私保护流量监控。为了满足VANET的独特功能和安全性要求,我们在提出的新颖解决方案中引入了同态加密,数据扰动和超增序列,以解决高效且保护隐私的流量监控的挑战。安全分析表明,我们的解决方案不仅可以保留车辆的身份,位置和数据隐私,而且还可以有效缓解串通攻击。此外,实验结果证实了我们的解决方案在计算和通信成本方面的效率。最后但并非最不重要的一点是,讨论了一些有趣的挑战以及潜在的解决方案,旨在吸引这一新兴领域的更多研究。
更新日期:2019-12-25
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