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Monday, September 9, 2019 at 11:00am to 12:00pm
Cornell Tech, Bloomberg Center 161 2 West Loop Road New York, NY 10044
"Safeguarding Privacy in Sequential Decision-Making Problems"
With the increasing ubiquity of large-scale surveillance and data analysis infrastructures, privacy has become a pressing concern in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. More concretely, we are interested in ways that an agent can take actions that make progress towards a certain goal, while minimizing the information revealed to a powerful adversary who monitors these actions. We will examine two well-known decision problems (path planning and active learning), and in both cases establish sharp tradeoffs between obfuscation effort and level of privacy. As a byproduct, our analysis also leads to simple yet provably optimal obfuscation strategies. Based on joint work with Kuang Xu (Stanford) and Zhi Xu (MIT).
Bio: John N. Tsitsiklis received the B.S. degree in mathematics and the B.S., M.S., and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 1980, 1980, 1981, and 1984, respectively. His research interests are in systems, optimization, communications, control, and operations research. He has coauthored four books and several journal papers in these areas.
He is currently a Clarence J. Lebel Professor with the Department of Electrical Engineering and Computer Science, at MIT, where he serves as the director of the Laboratory for Information and Decision Systems. He is a member of the National Academy of Engineering and holds three honorary doctorates. Among other distinctions, he is a recipient of the ACM SIGMETRICS Achievement Award (2016), the IEEE Control Systems Award (2018) and the INFORMS von Neumann Theory Prize (2018).