Quality of Private Information (QoPI) model for effective representation and prediction of privacy controls in mobile computing

Most existing privacy-control methods in mobile computing support only binary and static privacy controls; therefore, it is usually difficult for mobile users to make use of effective privacy controls by considering both the necessity of an application and the types and quality of private information to be provided to the application under dynamic usage scenarios. In this paper, we define a quality of private information (QoPI) model to represent various types and quality levels of users' private information required by mobile applications. Using the QoPI model, we can also represent contextual properties that might affect the selection of the types and quality of private information in dynamic mobile computing situations. Users' common privacy-control patterns can be characterized, represented, and managed by using this model, and we can assist users in achieving context-aware and personalized privacy control. We evaluate the effectiveness of using the QoPI model by analyzing the data that we collected from users while allowing them to consider practical mobile computing situations. The analysis results show that the users actively utilized the fine-grained (multi-level) privacy controls supported by using the QoPI model, and their privacy-control patterns could be effectively collected and predicted based on this model. The results also show that consideration of contextual properties is essential for improving the accuracy and time performance of predicting an appropriate QoPI level to be used when a user accesses a mobile application.