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Advertisers are increasingly turning to fingerprinting techniques to trackusers across the web. As web browsing activity shifts to mobile platforms,traditional browser fingerprinting techniques become less effective; however,device fingerprinting using built-in sensors offers a new avenue for attack. Westudy the feasibility of using motion sensors to perform device fingerprintingat scale, and explore countermeasures that can be used to protect privacy.  We perform a large-scale user study to demonstrate that motion sensorfingerprinting is effective with even 500 users. We also develop a model toestimate prediction accuracy for larger user populations; our model provides aconservative estimate of at least 12% classification accuracy with 1users. We then investigate the use of motion sensors on the web and find,distressingly, that many sites send motion sensor data to servers for storageand analysis, paving the way to potential fingerprinting. Finally, we considerthe problem of developing fingerprinting countermeasures; we evaluate apreviously proposed obfuscation technique and a newly developed quantizationtechnique via a user study. We find that both techniques are able todrastically reduce fingerprinting accuracy without significantly impacting theutility of the sensors in web applications.

Smartphone Fingerprinting Via Motion Sensors: Analyzing Feasibility at Large-Scale and Studying Real Usage Patterns