Increasing amounts of data are being collected about users, and increasingly sophisticated analytics are being applied to this data for various purposes. Privacy analytics are machine learning and data mining algorithms applied by end-users to their data for the purpose of helping them manage both private information and their self-presentation. This research develops privacy analytics that help users answer three interconnected questions about their online persona (1) What data does the user consider sensitive, and in what contexts should one share it?; (2) What does the data say about the user; and (3) Who knows what? These privacy analytics introduce a novel, inverse data mining problem where users analyze their data to estimate the conclusions the data will produce when incorporated into larger data sets. This project designs new algorithms for quantitative and automated methods to detect privacy-related phenomena that have been observed qualitatively. These algorithms support the development of usable privacy enhancing technologies and will give users tools to cope with and manage their data in a complicated data environment. These tools will provide awareness to users about how their data is being used. These analytics will also help answer questions critical to the development of privacy law and policy.
This work involves approximately twenty-five undergraduates in research activities, exposing them to research methods and privacy issues. This project also develops novel educational materials including course offerings for an interdisciplinary master's program in security and educational tools for use by the general public to bridge the digital divide.
