Appstract: On-The-Fly App Content Semantics With Better Privacy - Fernandes, Earlence; Riva, Oriana; Nath, Suman (ACM Annual International Conference on Mobile Computing and Networking (MobiCom'16), 2016)
Services like Google Now on Tap and Bing Snapp enable new user experiences by understanding the semantics of contents that users consume in their apps. These systems send contents of currently displayed app pages to the cloud to identify relevant entities (e.g., a movie) appearing in the current page and show information related to such entities (e.g., local theaters playing the movie). These new experiences come with privacy concerns as they can send sensitive on-screen data (bank details, medical data, etc.) to the cloud. We propose a novel approach that efficiently extracts app content semantics on the device, without exfiltrating user data. Our solution consists of two phases: an offline, user-agnostic, in-cloud phase that automatically annotates apps’ UI elements with stable semantics, and a lightweight on-device phase that assigns semantics to captured app contents on the fly, by matching the annotations. With this automatic approach we annotated 100+ food, dining, and music apps, with accuracy over 80%. Our system implementation for Android and Windows Phone—Appstract—incurs minimal runtime overhead. We built eight use cases on the Appstract framework.