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Tell us what you mean by "Augmented Reality" and what this research shows us about its consequences and implications?
Acquisti: We use the term, "augmented reality," in an expanded sense, to refer to the merging of online and offline data that new technologies make possible. When I can recognize your face in the street, using a face recognizer, and also find your Facebook profile that way, I can not only identify you, but also infer additional sensitive information about you (such as, in our third experiment, your Social Security number). Effectively, we start from an anonymous face in the street, and we end up with very sensitive information about that person. This is the kind of future we are walking into whether we like it or not, and the future consequences and implications of this seamless blending of online and offline data are anybody's guess.
You also mentioned scalability issues in your presentation. In what ways may scalability impact this trend?
Acquisti: As of today, automated face recognition is still pretty bad, but it keeps improving. If you look at the technological trends in cloud computing, the accuracy of face recognizers, and online self-disclosures, it is hard not to conclude that what we present today as a proof-of-concept in our study; will tomorrow become as common as everyday text-based searches on a search engine.
What are the immediate or near term implications of this study for users of Facebook and social media both personally and professionally? And likewise, what are the immediate or near term implications for organizations in regard to their workforce? What do governments and advocacy groups need to get their minds around in regard to these technological capabilities?
Acquisti: There is no obvious answer or solution to the privacy concerns raised by widely available face recognition. Google's Eric Schmidt observed that, in the future, young individuals may be inclined to change their names to disown youthful improprieties. It is much harder, however, to change someone's face. Other than adapting to a world where every stranger in the street could quite accurately predict your credit score and sexual orientation, we need to think about policy solutions that can balance the benefits and risks of peer-based face recognition. Self-regulation is not going to work.
Richard Power is a Distinguished Fellow at Carnegie Mellon University CyLab. He writes and speaks on security, risk and sustainability issues. Power is the author of seven books, and has conducted executive briefings and led security training in forty countries. He also writes a frequent column for CSO Magazine, and serves on its Technical Advisory Board.