  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.
