Inhalt des Dokuments
Predicting tap locations on touch screens in the field using accelerometer and gyroscope sensor readings
LOCATION: TEL, Auditorium 3 (20th floor),
Ernst-Reuter-Platz 7, 10587 Berlin
Date/Time: 06.11.2017, 14:15-15:00
SPEAKER: Emanuel Schmitt (TU Berlin)
Recent research has shown that the location of touch screen taps on modern smartphones and tablet computers can be identified based on sensor recordings from the device’s accelerometer and gyroscope. This security threat implies that an attacker could launch a background process on the mobile device and send the motion sensor readings to a third party vendor for further analysis. Even though the location inference is a non-trivial task requiring machine learning algorithms in order to predict the tap location, previous research has shown that PINs and passwords of users could be successfully obtained. However, as the tap location inference was only shown for taps generated in a controlled setting not reflecting the environment users naturally engage with their smartphones, the attempts in this work bridge this gap.
In this work, I propose TapSensing, a data acquisition system designed to collect touch screen tap event information with corresponding accelerometer and gyroscope readings. Having performed a data acquisition study with 27 subjects and 3 different iPhone models, a total of 45,000 labeled taps could be acquired from a laboratory and field environ- ment enabling a direct comparison of both. The overall findings show that tap location inference is generally possible for data acquired in the field, hence, with a performance reduction of approximately 20% when comparing both environments. As the tap infer- ence has therefore been shown for a more realistic data set, this work yet again confirms that smartphone motion sensors could potentially be used to comprise the user’s privacy.