Tracking Mobile Web Users Through Motion Sensors: Attacks and Defenses
Tracking Mobile Web Users Through Motion Sensors: Attacks and Defenses
复制标题
DOI：
10.14722/ndss.2016.23390
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Anupam Das;N. Borisov;M. Caesar
中科院分区：
文献类型：
--
作者：
Anupam Das;N. Borisov;M. Caesar
现代智能手机包含运动传感器，例如加速度计和陀螺仪。这些传感器有许多有用的应用;然而，它们也可以通过测量信号中的异常来唯一地识别手机，这些异常是制造缺陷的结果。这种测量可以由网页发布者或广告商秘密进行，因此可以用于跟踪跨应用程序，网站和访问的用户。我们分析如何以及传感器指纹在现实世界的约束下工作。我们首先开发了一种高度准确的指纹识别机制，它结合了多个运动传感器，并利用听不见的音频刺激来提高检测。我们使用大量智能手机在实验室和公共条件下的测量来评估这种机制。然后，我们分析技术，以减轻传感器指纹，通过校准传感器，以消除信号异常，或通过添加噪声，混淆异常。我们评估了校准和混淆技术对分类器精度的影响;我们还研究了这种缓解技术如何影响运动传感器的实用性。
Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a result of manufacturing imperfections. Such measurements can be conducted surreptitiously by web page publishers or advertisers and can thus be used to track users across applications, websites, and visits. We analyze how well sensor fingerprinting works under realworld constraints. We first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of inaudible audio stimulation to improve detection. We evaluate this mechanism using measurements from a large collection of smartphones, in both lab and public conditions. We then analyze techniques to mitigate sensor fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. We evaluate the impact of calibration and obfuscation techniques on the classifier accuracy; we also look at how such mitigation techniques impact the utility of the motion sensors.