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We present the design and design rationale for the user interfaces for Privacy Enhancements for Android (PE for Android). These UIs are built around two core ideas, namely that developers should explicitly declare the purpose of why sensitive data is being used, and these permission-purpose pairs should be split by first party and third party uses. We also present a taxonomy of purposes and ways of how these ideas can be deployed in the existing Android ecosystem.
Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because
Access to privacy-sensitive information on Android is a growing concern in the mobile community. Albeit Google Play recently introduced some privacy guidelines, it is still an open problem to soundly verify whether apps actually comply with such rule
Environmental understanding capability of $textit{augmented}$ (AR) and $textit{mixed reality}$ (MR) devices are continuously improving through advances in sensing, computer vision, and machine learning. Various AR/MR applications demonstrate such cap
Android unlock patterns remain quite common. Our study, as well as others, finds that roughly 25% of respondents use a pattern when unlocking their phone. Despite known security issues, the design of the pattern interface remains unchanged since firs
In response to the Covid-19 pandemic, educational institutions quickly transitioned to remote learning. The problem of how to perform student assessment in an online environment has become increasingly relevant, leading many institutions and educator