车辆网络中的位置隐私方案:分类、比较分析、设计挑战和未来机遇

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-01-10 DOI:10.1145/3711681
Ikram Ullah, Munam Ali Shah, Abid Khan, Mohsen Guizani
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引用次数: 0

摘要

车辆自组织网络(VANETs)通过智能交通管理,更好地利用道路环境,为车辆驾驶员提供安全和便利,彻底改变了世界。尽管VANETs有很多有用的功能,但也存在一些隐私问题,这阻碍了他们在世界上实现更智能、更安全的交通。位置隐私是有效部署vanet的关键研究挑战之一。这个挑战可以使用假名而不是信标消息中的实际车辆身份来解决。为此,文献中介绍了许多位置隐私方案。在本文中,我们全面地回顾了现有的位置隐私方案,并给出了它们的综合分类。我们讨论了开发一个有效的位置隐私方案所面临的设计挑战。此外,基于不同的道路网络环境和参数,对现有的位置隐私技术进行了批判性分析。详细阐述了假名变更过程中的各种问题和挑战。最后,我们讨论了车载网络中位置隐私实现的未来趋势。
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Location Privacy Schemes in Vehicular Networks: Taxonomy, Comparative Analysis, Design Challenges, and Future Opportunities
Vehicular ad-hoc networks (VANETs) have revolutionized the world with smart traffic management, better utilizing the road environment, and providing safety and convenience to the vehicles’ drivers. Despite the useful features of VANETs, there are some privacy issues, which hinder their way toward achieving smarter and safer traffic in the world. Location privacy is one of the critical research challenges for the efficient deployment of VANETs. This challenge can be solved using a pseudonym instead of an actual vehicle identity in the beacon messages. For this purpose, many location privacy schemes are introduced in the literature. In this paper, we thoroughly review the existing location privacy schemes and present their comprehensive taxonomy. We discuss the design challenges for the development of an efficient location privacy scheme. Moreover, the existing location privacy techniques are critically analyzed based on diverse road network environments and parameters. Various issues and challenges regarding the pseudonym-changing process are elaborated in detail. Finally, we discuss the future trends for the implementation of location privacy in a vehicular network.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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