Name : Ting Yu
Ting Yu is an Associate Professor of the Department of Computer
Science at North Carolina State University. His current research interests are
data security and privacy, security policies, trust management and reputation
Ting Yu received his B.S. in 1997 from Peking University, his M.S. in 1998 from University of Minnesota, and his Ph.D. in 2003 from the University of Illinois at Urbana-Champaign, all in computer science.
Ting Yu received the David J. Kuck Outstanding Thesis Award in 2004, and a Faculty Early Career Development (CAREER) Award from the National Science Foundation in 2008.
Nick Koudas, Divesh Srivastava, Ting Yu and Qing Zhang, “Distribution-based Microdata Anonymization,” To appear in the proceedings of International Conference on Very Large Data Bases (VLDB), 2009.
Graham Cordome, Divesh Srivastava, Ting Yu and Qing Zhang, “Anonymizing Bipartite Graph Data with Safe Grouping,” In the proceedings of International Conference on Very Large Data Bases (VLDB)., 2008.
Title of Project : On the Anonymization of Structure-Public Online Social Networks
Preserving the privacy of individual users is a major concern when social network data are published or shared with others for research and business analysis. Most existing work focuses on protecting user identities (i.e., whether a particular user is a member of a social network) or user relationships (e.g., whether Alice and Bob are friends in a social network). Meanwhile, we observe that many online social networks actually make their structure largely, if not totally, public. Consequently user identities and their relationships are public knowledge. Instead, it is the user profiles that often contain sensitive private information and should be protected while publishing such structure-public social networks. In this project, we formulate the problem of anonymizing structure-public social network data, and systematically investigate possible types of queries over a social network. We propose a permutation-based anonymization framework, which is more appropriate than topology modification techniques such as edge deletion/addition and node merging. We conduct theoretical analyses and comprehensive experiments to show that the proposed permutation-based anonymization techniques can better preserve data utility while protecting privacy, thus achieving a more acceptable tradeoff between utility and privacy.