移动IMU通过车辆转弯显示驾驶员身份
Mobile IMUs Reveal Driver's Identity From Vehicle Turns
摘要：
随着辅助或自动驾驶的车辆操纵数据变得丰富，其隐私侵犯/泄露的含义已成为越来越多的关注。特别地，如果驾驶员的身份可以与从他的移动或可穿戴设备收集的数据相关联，则用于对驾驶员进行指纹识别的表面将显著扩展，所述移动或可穿戴设备在世界范围内广泛部署并且具有越来越强的感测能力。根据这一趋势，本文研究了一个快速新兴的驾驶数据源，具有驾驶员的隐私影响。我们首先表明，这种隐私威胁可以通过任何带有IMU的移动终端来实现（例如，陀螺仪和加速计）。然后，我们介绍了Dri-Fi（驾驶员指纹），这是一种驾驶数据分析引擎，可以根据车辆转弯对驾驶员进行指纹识别。Dri-Fi基于仅在车辆转弯期间获取的IMU数据实现这一点。与现有的驱动程序指纹识别方案相比，这种方法大大扩展了攻击面。从这些数据中，Dri-Fi提取了三个新的特征--沿转弯轴的加速度、其偏差和偏航率偏差--并利用它们来识别驾驶员。我们的广泛评估表明，配备了Dri-Fi的对手可以在一个转弯内正确识别驾驶员，在12个，8个和5个驾驶员中分别具有74.1%，83.5%和90.8%的准确率-典型的直系亲属或亲密朋友圈。此外，通过对多个转弯的测量，对手可以分别在12、8和5个驾驶员上实现高达95.3%、95.4%和96.6%的准确度。
英文摘要：
As vehicle maneuver data becomes abundant for assisted or autonomous driving, their implication of privacy invasion/leakage has become an increasing concern. In particular, the surface for fingerprinting a driver will expand significantly if the driver's identity can be linked with the data collected from his mobile or wearable devices which are widely deployed worldwide and have increasing sensing capabilities. In line with this trend, this paper investigates a fast emerging driving data source that has driver's privacy implications. We first show that such privacy threats can be materialized via any mobile device with IMUs (e.g., gyroscope and accelerometer). We then present Dri-Fi (Driver Fingerprint), a driving data analytic engine that can fingerprint the driver with vehicle turn(s). Dri-Fi achieves this based on IMUs data taken only during the vehicle's turn(s). Such an approach expands the attack surface significantly compared to existing driver fingerprinting schemes. From this data, Dri-Fi extracts three new features --- acceleration along the end-of-turn axis, its deviation, and the deviation of the yaw rate --- and exploits them to identify the driver. Our extensive evaluation shows that an adversary equipped with Dri-Fi can correctly fingerprint the driver within just one turn with 74.1%, 83.5%, and 90.8% accuracy across 12, 8, and 5 drivers --- typical of an immediate family or close-friends circle --- respectively. Moreover, with measurements on more than one turn, the adversary can achieve up to 95.3%, 95.4%, and 96.6% accuracy across 12, 8, and 5 drivers, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
