Elastic pathing: your speed is enough to track you
Elastic pathing: your speed is enough to track you
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DOI：
10.1145/2632048.2632077
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Bernhard Firner;Shridatt Sugrim;Yulong Yang;J. Lindqvist
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文献类型：
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作者：
Bernhard Firner;Shridatt Sugrim;Yulong Yang;J. Lindqvist
今天，人们有机会选择加入基于使用的汽车保险，通过允许公司监控他们的驾驶行为来降低保费。一些公司声称只测量速度数据以保护隐私。通过我们的弹性路径算法，我们表明，只需收集他们的速度数据并知道他们的家庭位置，就可以跟踪驾驶员，保险公司也是这样做的，其准确性构成了隐私侵犯。为了证明该算法的现实世界的适用性，我们评估了其性能与数据集从中央新泽西和西雅图，华盛顿，代表郊区和城市地区。在新泽西数据集（254条迹线）中，我们的算法预测的目的地误差在250米以内，14%的迹线误差在500米以内，24%的迹线误差在500米以内。对于西雅图数据集（691条轨迹），我们同样预测了目的地，误差分别为13%和26%，误差在250米和500米以内。我们的工作表明，这些保险计划能够严重侵犯隐私。
Today, people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely collecting their speed data and knowing their home location, which insurance companies do, with an accuracy that constitutes privacy intrusion. To demonstrate the algorithm's real-world applicability, we evaluated its performance with datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. Our algorithm predicted destinations with error within 250 meters for 14% traces and within 500 meters for 24% traces in the New Jersey dataset (254 traces). For the Seattle dataset (691 traces), we similarly predicted destinations with error within 250 and 500 meters for 13% and 26% of the traces respectively. Our work shows that these insurance schemes enable a substantial breach of privacy.