Privacy Challenges With Protecting Live Vehicular Location Context
Privacy Challenges With Protecting Live Vehicular Location Context
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DOI：
10.1109/access.2020.3038533
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
M. Bradbury;Phillip Taylor;U. Atmaca;C. Maple;N. Griffiths
中科院分区：
文献类型：
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作者：
M. Bradbury;Phillip Taylor;U. Atmaca;C. Maple;N. Griffiths
未来的智能交通系统(ITS)将要求车辆配备专用短程通信(DSRC)。有了这些DSRC功能，新的隐私威胁正在出现，几乎没有经验和廉价组件的威胁参与者可以利用这些威胁。然而，这些隐私威胁的来源不仅限于车辆及其通信，还延伸到司机和乘客携带的非车载设备。现有工作的一个缺点是，当试图保护位置隐私时，它往往集中在隐私泄露的特定方面。在这样做时，隐私威胁之间的交互不被考虑。在这项工作中，我们通过考虑位置隐私可能被泄露的许多不同方式来调查车辆的隐私表面。随后，我们确定了保护隐私的技术，并且针对单个威胁媒介提供位置隐私是不够的。使用一种方法来计算隐私保护技术的交互作用，以强调需要考虑更广泛的威胁情况以及协作技术以确保在可能的情况下针对多个隐私威胁源提供位置隐私。
Future Intelligent Transport Systems (ITS) will require that vehicles are equipped with Dedicated Short Range Communications (DSRC). With these DSRC capabilities, new privacy threats are emerging that can be taken advantage of by threat actors with little experience and cheap components. However, the origins of these privacy threats are not limited to the vehicle and its communications, but extend to non-vehicular devices carried by the driver and passengers. A shortcoming of existing work is that it tends to focus on a specific aspect of privacy leakage when attempting to protect location privacy. In doing so, interactions between privacy threats are not considered. In this work, we investigate the privacy surface of a vehicle by considering the many different ways in which location privacy can be leaked. Following this, we identify techniques to protect privacy and that it is insufficient to provide location privacy against a single threat vector. A methodology to calculate the interactions of privacy preserving techniques is used to highlight the need to consider the wider threat landscape and for techniques to collaborate to ensure location privacy is provided against multiple sources of privacy threats where possible.