报告题目：Privacy-Preserving Regression Analysis
内容摘要：Data mining and sharing technologies have enabled organizations to extract useful knowledge from data in order to gain competitive advantages. While successful applications of data mining/sharing 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. A variety of approaches have been proposed to resolve the conflict between data mining/sharing and privacy protection. This talk first provides an overview of the current state-of-the-art in this research area and then presents a research on privacy-preserving regression analysis. We demonstrate that regression trees, a popular data-analysis and data-mining technique, can be used to effectively reveal individuals’ sensitive data. We propose a new approach to counter such a “regression attack.” Our approach assesses the sensitive value disclosure risk in the process of building a regression tree model. We also propose a dynamic value-concatenation method for anonymizing data, which better preserves data utility than existing methods. An experimental study is conducted using real-world financial, economic and healthcare data. The results of the experiments demonstrate the effectiveness of the proposed approach.
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. Dr. Li’s research focuses on data mining and analytics, data privacy, and information economics. He has received funding for his research from National Institutes of Health (NIH) and National Science Foundation (NSF). His work has appeared in Management Science, Information Systems Research, MIS Quarterly, Operations Research, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Communications of the ACM, INFORMS Journal on Computing, Decision Support Systems, and European Journal of Operational Research, among others.