Privacy-Preserving Regression Analysis
时 间：2017年6月26日（周一）上午 11:00-12:00
地 点：体育外围平台APP紫金港校区行政楼1102会议室
主讲人：李晓白教授, 麻省大学洛威尔分校
主持人：王明征教授，体育外围平台APP
摘 要:
While successful applications of data mining/sharing technologies 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. This talk first provides an overview of the current state-of-the-art in the research stream on resolving the conflict between data mining/sharing and privacy protection, 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. Experimental results using real-world financial, economic and healthcare data, demonstrate the effectiveness of the proposed approach.
主讲人简介：
Prof. Xiaobai Li is a professor at manning business school, University of Massachusetts Lowell. His research focuses on data mining and analytics, and information economics. His work has appeared in Management Science, Information Systems Research, MIS Quarterly, Operations Research, Communications of the ACM, INFORMS Journal on Computing, Decision Support Systems, etc.
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