Publication Details
| Emanuel von Zezschwitz, Malin Eiband, Daniel Buschek, Sascha Oberhuber, Alexander De Luca, Florian Alt, Heinrich Hussmann
On Quantifying the Effective Password Space of Grid-based Unlock Gestures In MUM '16: Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia, Rovaniemi, Finnland (bib) |
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| We present a similarity metric for Android unlock patterns to quantify the effective password space of user-defined gestures. Our metric is the first of its kind to reflect that users choose patterns based on human intuition and interest in geometric properties of the resulting shapes. Applying our metric to a dataset of 506 user-defined patterns reveals very similar shapes that only differ by simple geometric transformations such as rotation. This shrinks the effective password space by 66% and allows informed guessing attacks. Consequently, we present an approach to subtly nudge users to create more diverse patterns by showing background images and animations during pattern creation. Results from a user study (n=496) show that applying such countermeasures can significantly increase pattern diversity. In addition, we found that the evaluation strategy has a strong impact on the measured effects. We conclude with implications for pattern choices and the design of enrollment processes. |
