SPEAKER: Dr. Yingbin Liang, Ph.D, Syracuse University
Secret key distribution is a well-known challenging security problem in wireless networks such as internet of things (IoT) systems and mobile-to-mobile (M2M) networks, due to their minimum infrastructures, dynamic node associations, and lack of computational power and memory. Information theoretic approach provides a promising framework for solving the secret key generation problem in these networks. The idea is to exploit the randomness resources naturally present in wireless networks for generating secret keys. Such an approach has numerous advantages including guarantee of provable security, light computational cost, and easy implementation for mobile devices.
In this talk, I will present our recent solutions of two fundamental key generation problems under the information theoretic framework. The first problem concerns joint generation of a secret key and a private key in order to simultaneously secure groupcast and unicast communications. We designed the optimal key generation scheme, which fully resolved this ten-year open problem. The second problem concerns the minimization of the number of nodes participating into the key generation process in order to achieve the best resource efficiency. We provided the solution for systems with arbitrary number of nodes, which significantly generalizes the previous understanding of only the three-node system. As conclusion of the talk, I will identify applications and new directions of information theoretic security.
Dr.Yingbin Liang received her Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2005. In 2005-2007, she was working as a postdoctoral research associate at Princeton University. In 2008-2009, she was an assistant professor at the University of Hawaii. Since December 2009, she has been on the faculty at Syracuse University, where she is an associate professor. Dr. Liang's research interests include information theory, wireless communication and networks, machine learning, statistical signal processing, and optimization.