Gesture Authentication for Smartphones: Evaluation of Gesture Password Selection Policies

Abstract

Touchscreen gestures are attracting research attention as an authentication method. While studies have showcased their usability, it has proven more complex to determine, let alone enhance, their security. Problems stem both from the small scale of current data sets and the fact that gestures are matched imprecisely -- by a distance metric. This makes it challenging to assess entropy with traditional algorithms. To address these problems, we captured a large set of gesture passwords (N=2594) from crowd workers, and developed a security assessment framework that can calculate partial guessing entropy estimates, and generate dictionaries that crack 23.13% or more gestures in online attacks (within 20 guesses). To improve the entropy of gesture passwords, we designed novel blacklist and lexical policies to, respectively, restrict and inspire gesture creation. We close by validating both our security assessment framework and policies in a new crowd-sourced study (N=4000). Our blacklists increase entropy and resistance to dictionary based guessing attacks.