This week Las Vegas hosted the Black Hat technical security conference, where researchers Alessandro Acquisti, Ralph Gross and Fred Stutzman presented a study demonstrating that basic facial recognition software can easily identify the faces of strangers both online and offline and connect that with “sensitive personal data” in real time. Mashable reports that such sensitive personal data includes predicting the Social Security numbers of strangers by combining facial recognition and publicly available images from social networking sites with SSN prediction algorithms.
The FAQ for the project explains, “if an individual’s face in the street can be identified using a face recognizer and identified images from social network sites such as Facebook or LinkedIn, then it becomes possible not just to identify that individual, but also to infer additional, and more sensitive, information about her, once her name has been (probabilistically) inferred.” The social networking info was collected without even signing in on the networks. The team was able to easily identify nearly a third of random students on a college campus with their methods.
“Faces of Facebook: Privacy in the Age of Augmented Reality” was funded by the National Science Foundation, the U.S. Army Research Office, the Carnegie Mellon Berkman Fund, Heinz College and the Carnegie Mellon CyLab.