Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting
This addresses privacy risks for smartphone users by preventing cross-application tracking, though it is incremental as it builds on existing fingerprinting and mitigation concepts.
The paper tackles the problem of smartphone fingerprinting via motion sensor anomalies by developing a mitigation approach using calibration and noise addition, resulting in a reduction of classifier accuracy from 95% to below 10% in some cases.
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.