Abstract
Mobile apps include privacy settings that allow their users to configure how their data should be shared. These settings, however, are often hard to locate and hard to understand by the users, even in popular apps, such as Facebook. More seriously, they are often set to share user data by default, exposing her privacy without proper consent. In this paper, we report the first systematic study on the problem, which is made possible through an in-depth analysis of user perception of the privacy settings. More specifically, we first conduct two user studies (involving nearly one thousand users) to understand privacy settings from the user’s perspective, and identify these hard-to-find settings. Then we select 14 features that uniquely characterize such hidden privacy settings and utilize a novel technique called semantics- based UI tracing to extract them from a given app. On top of these features, a classifier is trained to automatically discover the hidden privacy settings, which together with other innovations, has been implemented into a tool called Hound. Over our labeled data set, the tool achieves an accuracy of 93.54%. Further running it on 100,000 latest apps from both Google Play and third-party markets, we find that over a third (36.29%) of the privacy settings identified from these apps are “hidden”. Looking into these settings, we observe that they become hard to discover and hard to understand primarily due to the problematic categorization on the apps’ user interfaces and/or confusing descriptions. Further importantly, though more privacy options have been offered to the user over time, also discovered is the persistence of their usability issue, which becomes even more serious, e.g., originally easy-to-find settings now harder to locate. And among all such hidden privacy settings, 82.16% are set to leak user privacy by default. We provide suggestions for improving the usability of these privacy settings at the end of our study.
Fnu Suya
Assistant Professor
My research interests include machine learning for security and trustworthy machine learning.