6月17日 ：Li Xiong: Privacy-Preserving Data Sharing and Analytics with Differential Privacy
讲座题目：Privacy-Preserving Data Sharing and Analytics with Differential Privacy
主讲人：Li Xiong (Emory University)
主持人：查宏远 教授
开始时间：2016-6-17 周五 13:30—15:00
讲座地点：中北校区数学馆201报告厅
报告人简介：
Li Xiong is a Winship Distinguished Research Professor of Computer Science (and Biomedical Informatics) at Emory University. She holds a PhD from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from University of Science and Technology of China, all in Computer Science. She and her research group, Assured Information Management and Sharing (AIMS), conduct research that addresses both fundamental and applied questions at the interface of data privacy and security, spatiotemporal data management, and health informatics. She has published over 100 papers in premier journals and conferences including TKDE, VLDB, ICDE, CCS, and WWW, with three best paper awards. She currently serves as associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE) and regularly serves on program committees for data management and data security conferences. She is a recipient of a Woodrow Wilson Fellowship and industry awards from Google, IBM, and Cisco. Her research is supported by NSF, NIH, AFOSR (Air Force Office of Scientific Research), and PCORI (Patient-Centered Outcomes Research Institute).
报告内容：
While “Big Data” promises significant economic and social benefits, it also raises increasing privacy concerns. The traditional de-identification approach is subject to various re-identification and disclosure risks and does not provide sufficient privacy protection. In this talk, I will give an overview of our work on privacy preserving data sharing and analytics with the state-of-art differential privacy framework. I will present several technical solutions for handing different types of data including relational, sequential, and time series data with various data mining applications. I will also present case studies using medical and spatiotemporal data to demonstrate the feasibility as well as challenges of applying the differential privacy framework for biomedical and spatiotemporal applications.