PriApp-Install: Learning User Privacy Preferences on Mobile Apps’ Installation

Abstract

It is undeniable that smartphones play a vital role in our lives, as their applications (called apps) can be used to access a variety of services anytime and anywhere. Despite the benefits provided by mobile apps, there are risks connected to the release of personal and sensitive data. Understanding the potential privacy risks of installing an app based on its description or privacy policy could be challenging, especially for non-skilled users. In this paper, to assist users in their app selection process, we propose PriApp-Install, a privacy-aware app installation recommendation system. It leverages semi-supervised learning to learn individual privacy preferences w.r.t mobile app installation. Learning is done based on a rich set of features modeling both the app behavior w.r.t. personal data consumption and the benefits a user can get in installing the app. We have tested four different learning strategies on a real dataset by exploiting three participant groups: security and privacy experts, IT workers, and crowd workers. The obtained results show the effectiveness of our proposal.