Your Behaviors Reveal What You Need: A Practical Scheme Based on User Behaviors for Personalized Security Nudges

Leilei Qu, Ruojin Xiao, Wenchang Shi, Keman Huang, Bo Qin, Bin Liang

Abstract: Since personalization was introduced to security nudges, several approaches using the correlations between the General Decision-Making Styles (GDMS) and nudge effects have been proposed. However, the GDMS-based schemes do not apply to real systems well since it is challenging, if not impossible, to obtain the GDMS without psychological scales. Instead, we propose a practical scheme that leverages users’ system-use behaviors to personalize security nudges. To verify the effectiveness of the developed scheme, we analyze the data collected through two between-subjects lab experiments (N1<math><msub is="true"><mi is="true">N</mi><mn is="true">1</mn></msub></math> = 312, N2<math><msub is="true"><mi is="true">N</mi><mn is="true">2</mn></msub></math> = 696). By comparing the efficacy of the behavior-based and the GDMS-based approaches, we find that the behaviors outperform the GDMS in accurately predicting nudge effects, and more importantly, the behavior-based personalization scheme is comparably effective and more robust in improving nudge effects. This confirms that the behavior-based framework can be a practical and promising solution when implementing personalized nudge schemes to improve security behaviors.

External IDs:dblp:journals/compsec/QuXSHQL22