利用汽车控制器局域网消息还原驾驶轨迹，隐私风险极高
CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories
- 通过车速和油门位置数据构建加权图，重建驾驶路径
- 城市区域攻击成功率最高达90.59%，郊区达99.41%
- 适用于车辆隐私研究者，尤其关注车载系统安全的人
驾驶轨迹数据即便在现有防护措施下仍易遭隐私泄露。传统方法依赖全球定位系统（GPS）进行路径地图匹配，但面临信号中断问题。本文提出CAN-Trace，一种新型隐私攻击机制，利用控制器局域网（CAN）消息还原驾驶轨迹，对驾驶员长期隐私构成重大威胁。提出一种新轨迹重建算法，将车辆速度与油门踏板位置等CAN消息转换为反映不同驾驶状态的加权图；再通过图匹配算法，在道路网络中识别对应轨迹。设计新评估指标以应对数据缺失与匹配误差。实证验证显示，该方法在多种真实场景下表现优异：城市区域成功率达90.59%，郊区高达99.41%。
原文摘要 · Abstract (English)
Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers' long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.
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