Privacy Preserving Data Mining
Chris Clifton and Jaideep Vaidya - Department of Computer Science, Purdue University
Feb 27, 2002
AbstractData mining technology has emerged as a means of identifying patterns and trends from large quantities of data. Data mining and data warehousing go hand-in-hand: most tools operate by gathering all data into a central site, then running an algorithm against that data. Privacy considerations can prevent or limit sharing between sites, preventing us from creating a data warehouse. In this talk, we discuss this problem, and present solutions that enable us to obtain global data mining results without compromising local privacy. In particular, we will look at the problem of mining globally correct association rules without sharing local information.
About the SpeakerChris Clifton is an Associate Professor of Computer Science at Purdue University. He has a Ph.D. from Princeton University, and Bachelor\'s and Master\'s degrees from the Massachusetts Institute of Technology. Prior to joining Purdue in 2001, Chris had served as a Principal Scientist at The MITRE Corporation and as an Assistant Professor of Computer Science at Northwestern University. His research interests include data mining, data security, database support for text, and heterogeneous databases. This talk represents joint work with Murat Kantarcioglu and Jaideep Vaidya
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