It is undeniable that smartphones play a vital role in our lives, as their applications (apps) can be used to access various 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 modelling both the app behavior w.r.t. personal data consumption and the benefits a user can get in installing the app. We tested four 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.

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

Carminati, B;Ferrari, E
2022-01-01

Abstract

It is undeniable that smartphones play a vital role in our lives, as their applications (apps) can be used to access various 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 modelling both the app behavior w.r.t. personal data consumption and the benefits a user can get in installing the app. We tested four 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.
2022
978-3-031-21279-6
978-3-031-21280-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2146791
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