A Framework for Enhancing Account Recovery Using Dynamically Generated Location-based Questions

Abstract

This paper proposes an account recovery framework with location-based dynamic questions (ARF-L) to enhance account recovery processes. It leverages users' location histories to estimate visited places, frames questions by selecting suitable points from these locations, and then generates questions with corresponding choices. Preliminary experiments included a survey with 1,000 participants to gauge the social acceptability and prerequisites for generating such questions. A smaller scale manual question generation experiment with eight participants demonstrated high accuracy in responses and a favorable attitude towards our framework. Building on these insights, we proposed question generation algorithms and validated them on 38 participants, achieving responses with an 86% average accuracy rate. This study underscores the potential for using location history in security practices, although it also highlights challenges such as refining question generation algorithms. Our future efforts will address these challenges and ensure comprehensive reliability and applicability across different contexts of our approach.