Energy efficient semantic context model for managing privacy on smartphones

Modern smartphones are capable of gathering massive amounts of data about a user and her context. While this data is mostly utilized for providing services that are better suited to the user, user data and context leakage from smart phones can have disastrous results. This is especially true as most enterprises are going to a Bring-Your-Own-Device (BYOD) model for mobile devices such as smartphones. We recognize this change as a potential threat to data privacy, both of the user and also of corporations whose data is on employee owned phones. We describe a method to carry out energy efficient privacy preservation on a mobile smart-phone. Our work is based on a study of an Android smartphone’s component-wise energy consumption pattern and is based on a three-fold approach to ensure efficient execution of privacy policies, based on user and app context modeled using semantic web technologies. Currently we are working on integrating the knowledge acquired from experimental data into the Android framework.

See http://ebiq.org/p/617 for a short paper on this work.