AI

Supporting Privacy-Conscious App Update Decisions with User Reviews

Abstract

Smartphone app updates are critical to user security and privacy. New versions may fix important security bugs, which is why users should usually update their apps. However, occasionally apps turn malicious or radically change features in a way users dislike. Users should not necessarily always update in those circumstances, but current update processes are largely automatic. Therefore, it is important to understand user behaviors around updating apps and help them to make security-conscious choices. We conducted two related studies in this area. First, to understand users' current update decisions, we conducted an online survey of user attitudes toward updates. Based on the survey results, we then designed a notification scheme integrating user reviews, which we tested in a field study. Participants installed an Android app that simulated update notifications, enabling us to collect users' update decisions and reactions. We compared the effectiveness of our review-based update notifications with the permission-based notifications. Compared to notifications with permission descriptions only, we found our review-based update notification was more effective at alerting users of invasive or malicious app updates, especially for less trustworthy apps.