Mix-Zones as an Effective Privacy Enhancing Technique in Mobile and Vehicular Ad-hoc Networks

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Nirupama Ravi, C. M. Krishna, Israel Koren
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引用次数: 0

Abstract

Intelligent Transportation Systems (ITS) promise significant increases in throughput and reductions in trip delay. ITS makes extensive use of Connected and Autonomous Vehicles (CAV) frequently broadcasting location, speed, and intention information. However, with such extensive communication comes the risk to privacy. Preserving privacy while still exchanging vehicle state information has been recognized as an important problem.

Mix zones have emerged as a potentially effective way of protecting user privacy in ITS. CAVs are assigned pseudonyms to mask their identity; a mix zone is an area where CAVs can change their pseudonyms to resist being tracked.

In order to be effective, mix zone placement must take account of traffic flows. Also, since a mix zone can degrade throughput, mix zones must be used sparingly. Determining the number and placement of mix zones is a difficult dynamic optimization problem. This paper outlines the various approaches recently taken by researchers to deal with this problem.

混合区是移动和车载 Ad-hoc 网络中一种有效的隐私增强技术
智能交通系统(ITS)有望显著提高吞吐量并减少行程延误。智能交通系统广泛使用互联和自动驾驶车辆(CAV),经常广播位置、速度和意图信息。然而,如此广泛的通信也带来了隐私风险。在交换车辆状态信息的同时保护隐私已被视为一个重要问题。混合区作为一种在智能交通系统中保护用户隐私的潜在有效方法应运而生。为 CAV 分配假名以掩盖其身份;混合区是 CAV 可以更改其假名以防止被跟踪的区域。混合区的布置必须考虑交通流量,这样才能有效。此外,由于混合区会降低吞吐量,因此必须慎用混合区。确定混合区的数量和位置是一个困难的动态优化问题。本文概述了研究人员最近为解决这一问题而采取的各种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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