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A Randomization Approach to Privacy-Presrving Data Mining
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Abstract:
The goal of privacy preserving data mining is to develop accurate models without access to precise information in individual data records, thus resolving the conflict between privacy and data mining. The randomization approach exploits the difference between the level where we care about privacy, i.e., individual data, and the level where we run data mining algorithms, i.e., aggregated data. User data is randomized to disallow recovery of anything meaningful at the individual level, while still allowing recovery of aggregate information to build mining models. In this talk, I will give an introduction to the techniques underlying the randomization approach, and discuss application domains and open problems.
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Biography
Dr. Ramakrishnan Srikant is a Research Staff Member in the Intelligent Information Systems Research department at IBM Almaden Research Center, San Jose, CA. He received his M.S. and Ph.D. degrees in Computer Science from the University of Wisconsin, Madison, in 1996. He also has a B. Tech. degree in Computer Science & Engineering from the Indian Institute of Technology, Madras, India. His current research interests include privacy, data mining, and text and web mining.
Dr. Srikant was the Program Co-Chair of the 2001 ACM Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD-2001). He has also served as the Tutorials Chair of the 2003 ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining conference, and as the Industrial Track Co-Chair of the 2003 Pacific-Asia Conference on Knowledge Discovery and Data Mining. Dr. Srikant was named IBM Research Division Master Inventor in 1999. He has received 2 Outstanding Technical Achievement Awards for his contributions to the design and development of Intelligent Miner. He has also received 5 Invention Achievement Awards for his patenting activities (12 issued and 9 pending patents).
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