应西安交通大学信息工程科学研究中心的邀请, 美国University of Massachusetts Lowell Xiaobai Li教授将于2011年12月14日上午9:00于科学馆207会议室作题为

“Data Mining, Data Sharing, and Information Privacy:

Recent Development and a Data-Masking Research Framework”

的学术报告，欢迎广大师生积极参加.

报告人简介

Dr. Xiaobai Li is a Professor of Information Systems in the Department of Operations and Information Systems at the University of Massachusetts Lowell, USA. He received his Ph.D. in management science from the University of South Carolina, USA, in 1999. His research focuses on data mining, information privacy, and information economics. His work has appeared in Operations Research, Information Systems Research, Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Automatic Control, Decision Support Systems, INFORMS Journal on Computing, European Journal of Operational Research, among others. He has received funding for his research from National Institute of Health (NIH) and National Science Foundation (NSF), USA.

报告内容摘要

Data mining and sharing technologies have enabled organizations to extract useful knowledge from data in order to better understand and serve their customers, and thus gain competitive advantages. While successful applications of data mining are encouraging, there are growing concerns about invasions to privacy of personal information by information technology in general, and by data mining/sharing in particular. Recently, some data mining-based counter-terrorism programs developed by the U.S. governments were terminated due to strong opposition by the public and privacy advocates. A variety of approaches have been proposed to resolve the conflict between data mining/sharing and privacy protection. This talk will begin with an overview of the current state-of-the-art in this cutting-edge research area, followed by a data-masking framework for privacy protection in data mining and data sharing, developed by the speaker and his collaborators.

This research area is cross-disciplinary in nature. It has attracted researchers from broad areas of background, including information systems, computer science and engineering, statistics, operations research, economics, and social sciences. We will introduce various approaches from different perspectives, and explain how these different approaches interact and complement each other.