Safeguarding Privacy in Dynamic Decision-Making Problems

The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for her goal, while simultaneously ensuring these actions do not inadvertently reveal her private information, even when observed and analyzed by a powerful adversary? We will examine two well-known decision problems (path planning and online learning), and in both cases establish sharp, information-theoretic complexity vs. privacy tradeoff. As a by-product, our analysis also leads to simple yet provably efficient algorithms for both the decision maker and eavesdropping adversary. Based in part on joint work with John N. Tsitsiklis and Zhi Xu (MIT).

Bio: Kuang Xu was born in Suzhou, China. He is an Assistant Professor of Operations, Information and Technology at the Stanford Graduate School of Business, Stanford University. He received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. His research interests lie in the fields of applied probability theory, optimization, and operations research, seeking to understand fundamental properties and design principles of large-scale stochastic systems, with applications in queueing networks, healthcare, privacy, and statistical learning theory. He has received several awards including a First Place in INFORMS George E. Nicholson Student Paper Competition, a Best Paper Award, as well as a Kenneth C. Sevcik Outstanding Student Paper Award from ACM SIGMETRICS.

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