In this talk, I will try to link two streams of research: the behavioural economics of privacy and the mining of data from online social networks. First, I will highlight how research in economics and behavioral economics can help us make sense of apparent inconsistencies in privacy decision-making, and will present results from a variety of experiments conducted in this field at Carnegie Mellon University. Then, I will discuss the technical feasibility and privacy implications of combining publicly available Web 2.0 images with off-the-shelf face recognition technology, for the purpose of large-scale, automated individual re-identification. Combined, the results highlight the behavioral, technological, and economic issues raised by the convergence of mining technologies and online social networks, and raise questions about the future of privacy in an “augmented reality” world.
To book
Everyone welcome, to book a place, please email Virginia Harris at ieevents@imperial.ac.uk. Venue will be confirmed on registration.
Speaker Biography
Alessandro Acquisti is an Associate Professor of Information Systems and Public Policy at the Heinz College, Carnegie Mellon University, and the co-director of the CMU Center for Behavioral Decision Research (CBDR). His research has initiated the application of behavioral economics to the study of privacy and information security, as well as privacy in online social networks, and privacy “nudges.” Alessandro has been the recipient of the PET Award for Outstanding Research in Privacy Enhancing Technologies, the IBM Best Academic Privacy Faculty Award, and multiple Best Paper awards. Alessandro’s research on privacy is eminently interdisciplinary: his manuscripts have been published in leading journals across multiple disciplines (including the Proceedings of the National Academy of Science, the Journal of Consumer Research, Marketing Science, Information Systems Research, the Journal of Comparative Economics, ACM Transactions on the Internet). Several of his findings have been featured in media outlets such as the New York Times, the Wall Street Journal, the Washington Post, CNN, NPR, and others. His 2009 study on the predictability of Social Security numbers (SSNs) was featured in the “Year in Ideas” issue of the NYT Magazine, and has contributed to the change in the assignment scheme of SSNs, announced in 2011 by the US Social Security Administration. His research has been funded by the National Science Foundation, Transcoop Foundation, Microsoft, and Google. Alessandro holds a PhD from UC Berkeley, and Master degrees from UC Berkeley, the London School of Economics, and Trinity College Dublin.