I will present the results of a series of experiments connecting research on consumer privacy decision making and research on privacy in online social networks. In particular, I will discuss 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; and I will discuss whether current "notice and consent" approaches to privacy policy are adequate means for privacy protection in this context.  Through some of the experiments, I will highlight 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. With another proof-of-concept experiment, I will illustrate 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. 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.

Alessandro Acquisti is an Associate Professor at the Heinz College, Carnegie Mellon University (CMU) and co-director of CMU Center for Behavioral and Decision Research.  He investigates the economics of privacy. His studies have spearheaded the application of behavioral economics to analysis of privacy and information security decision making, and the analysis of privacy and disclosure behavior in online social networks. Alessandro is recipient of the PET Award for Outstanding Research in Privacy Enhancing Technologies, IBM Best Academic Privacy Faculty Award, multiple Best Paper awards, Heinz College School of Information's Teaching Excellence Award. He has testified before the U.S. Senate and House committees on issues related to privacy policy and consumer behavior, and was a TED Global 2013 speaker. His findings have been featured in national and international media outlets, including the Economist, New York Times, Wall Street Journal, Washington Post, Financial Times, Wired.com, NPR, and CNN. His 2009 study on the predictability of Social Security numbers was featured in the "Year in Ideas" issue of the NYT Magazine (the SSNs assignment scheme was changed by the US Social Security Administration in 2011). Alessandro holds a PhD from UC Berkeley, Master degrees from UC Berkeley, the London School of Economics, and Trinity College Dublin. He has held visiting positions at the Universities of Rome, Paris, and Freiburg (visiting professor); Harvard University (visiting scholar); University of Chicago (visiting fellow); Microsoft Research (visiting researcher); and Google (visiting scientist). He has been a member of the National Academies' Committee on public response to alerts and warnings using social media.

Hosted by: Dr. Venkat Venkatakrishnan, Computer Science  and   Dr. Steven Jones, Communication