Privacy in the Age of Augmented Reality

We investigate the feasibility of combining publicly available Web 2.0 data with off-the-shelf face recognition software for the purpose of large-scale, automated individual re-identification. Two experiments demonstrated the ability of identifying strangers online (on a dating site where individuals protect their identities by using pseudonyms) and offline (in a public space), based on photos made publicly available on a social network site: combining face recognition and publicly available data from Facebook profiles, we re-identified 10% of users of a popular dating site and over 30% of students walking on the campus of a North-American college. A third proof-of-concept experiment illustrated the ability of inferring strangers’ personal or sensitive information (their interests and Social Security numbers) from their faces, by combining face recognition, data mining algorithms, and statistical re-identification techniques: starting merely from photos of their faces, we correctly identified interests for all the students who had participated in the previous experiment, and, in some cases, the first five digits of their Social Security numbers. The results highlight the implications of the inevitable convergence of face recognition technology and increasing online self-disclosures, and the emergence of “personally predictable” information. They raise questions about the future of privacy in an “augmented” reality world in which online and offline data will seamlessly blend.