On the Strength of Privacy Metrics for Vehicular Communication
On the Strength of Privacy Metrics for Vehicular Communication
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DOI：
10.1109/tmc.2018.2830359
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发表时间：
2019-02
影响因子：
7.9
通讯作者：
Yuchen Zhao;Isabel Wagner
中科院分区：
文献类型：
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作者：
Yuchen Zhao;Isabel Wagner
车载通信在不久的将来的汽车运输中发挥着关键作用，具有提高交通安全性和无线软件更新等前景。然而，车辆通信可能暴露驾驶员的位置，从而带来隐私风险。在车载通信中，人们提出了许多保护隐私的方案，其有效性通常通过隐私度量来评估。然而，据我们所知，（1）不同的隐私指标从未相互比较过，（2）这些指标的强度是未知的。在本文中，我们评估和比较了41个隐私度量的强度，在四个新的标准：隐私度量应该是单调的，即，表示隐私性降低，对手实力增强;它们的值应均匀分布在较大的值范围内，以支持场景内的可比性;它们应在交通状况之间共享大部分值范围，以支持场景间的可比性。我们评估所有四个标准的真实的和合成流量与国家的最先进的对手模型，并创建一个排名的隐私指标。我们的研究结果表明，没有一个单一的指标占主导地位，在所有的标准和交通条件。因此，我们建议使用指标套件，即，在评估新的隐私增强技术时，隐私指标的组合。
Vehicular communication plays a key role in near-future automotive transport, promising features such as increased traffic safety and wireless software updates. However, vehicular communication can expose drivers’ locations and thus poses privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually evaluated with privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we evaluate and compare the strength of 41 privacy metrics in terms of four novel criteria: Privacy metrics should be monotonic, i.e., indicate decreasing privacy for increasing adversary strength; their values should be spread evenly over a large value range to support within-scenario comparability; and they should share a large portion of their value range between traffic conditions to support between-scenario comparability. We evaluate all four criteria on real and synthetic traffic with state-of-the-art adversary models and create a ranking of privacy metrics. Our results indicate that no single metric dominates across all criteria and traffic conditions. We therefore recommend to use metrics suites, i.e., combinations of privacy metrics, when evaluating new privacy-enhancing technologies.