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Jekyll and Hyde: On The Double-Faced Nature of Smart-Phone Sensor Noise Injection
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Datum
2018
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Köllen Druck+Verlag GmbH
Zusammenfassung
To combat privacy attacks that exploit the motion and orientation sensors embedded in
mobile devices, a number of recent works have proposed noise injection schemes that degrade the
quality of sensor data. Much as these schemes have been shown to thwart the attacks, the impact of
noise injection on continuous authentication schemes proposed for mobile and wearable devices has
never been studied. In this paper, we empirically tackle this question based on two widely studied
continuous authentication applications (i.e., gait and handwriting authentication). Through a series
of machine learning and statistical techniques, we show that the thresholds of noise needed to overcome
the attacks would significantly degrade the performance of the continuous authentication applications.
The paper argues against noise injection as a defense against attacks that exploit motion
and orientation sensor data on mobile and wearable devices.