Super-Ego: A framework for privacy-sensitive bounded context-awareness

Context-awareness enables applications to better streamline and personalize their service according to the current situation of the user. However, the user's information used by context-aware applications, such as the user's current location, is inherently private and sensitive. Using this information without proper control by the user can lead to privacy risks and might harm the trust users have in the context-aware application. To address this tradeoff between the effectiveness and privacy, we present Super-Ego, a framework for at-hoc management of access to location information in ubiquitous environment. Using this framework, we model and evaluate different decision strategies for managing mobile application's access to location context. The strategies we test are based on automatic algorithms that use knowledge about historical disclosure of locations by large number of users, with the optional delegation of some of the decisions to the user. We evaluate the system empirically, using people's detailed location trails from public resources, augmented with simulated data about sharing behavior. Our results reflect on an interesting tradeoff between automation and accuracy, which can enable the design of efficient and usable approaches to privacy-sensitive context-aware applications.