探索减轻基于传感器的智能手机指纹识别的方法
Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting
摘要：
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，这些异常是由制造缺陷造成的。这种测量可以在浏览器中秘密进行，并可用于跟踪跨应用程序，网站和访问的用户。我们分析了通过校准传感器以消除信号异常或通过添加混淆异常的噪声来减轻此类设备指纹识别的技术。为此，我们首先开发了一种高度精确的指纹识别机制，该机制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。然后，我们从大量智能手机中收集测量结果，并评估校准和混淆技术对分类器准确性的影响。
英文摘要：
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 from manufacturing imperfections. Such measurements can be conducted surreptitiously in the browser and can be used to track users across applications, websites, and visits. We analyze techniques to mitigate such device fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. To do this, we first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of (inaudible) audio stimulation to improve detection. We then collect measurements from a large collection of smartphones and evaluate the impact of calibration and obfuscation techniques on the classifier accuracy.
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。
