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Modern smartphones contain motion sensors, such as accelerometers andgyroscopes. These sensors have many useful applications; however, they can alsobe used to uniquely identify a phone by measuring anomalies in the signals,which are a result from manufacturing imperfections. Such measurements can beconducted surreptitiously in the browser and can be used to track users acrossapplications, websites, and visits.  We analyze techniques to mitigate such device fingerprinting either bycalibrating the sensors to eliminate the signal anomalies, or by adding noisethat obfuscates the anomalies. To do this, we first develop a highly accuratefingerprinting mechanism that combines multiple motion sensors and makes use of(inaudible) audio stimulation to improve detection. We then collectmeasurements from a large collection of smartphones and evaluate the impact ofcalibration and obfuscation techniques on the classifier accuracy.

Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting