Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/BIOSIG.2018.8553043 | 2018 International Conference of the Biometrics Special Interest Group (BIOSIG) |
Keywords | Field | DocType |
continuous authentication,gait authentication,handwriting authentication,wearables and mobile phones | Computer vision,Microsoft Windows,Authentication,Noise measurement,Computer science,Accelerometer,Feature extraction,Real-time computing,Exploit,Mobile device,Artificial intelligence,Wearable technology | Conference |
ISBN | Citations | PageRank |
978-1-5386-6007-2 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Matovu | 1 | 1 | 1.71 |
| Abdul Serwadda | 2 | 214 | 13.71 |
| David Irakiza | 3 | 1 | 1.03 |
| Isaac Griswold-Steiner | 4 | 6 | 3.54 |