| Title: | Privacy Protection in Data Integration |
| Date: |
May 16, 2006 (Tuesday)
|
| Time: |
11:00 a.m. - 12:00 noon
|
| Venue: |
ERB513, William M.W. Mong Engineering Building,
The Chinese University of Hong Kong, Shatin, N.T. |
| Speaker: |
Prof. Ke Wang
School of Computing Science Simon Fraser University Canada |
I will present two recent works on privacy protection. In the first work, two parties wish to integrate their private databases, provided that their privacy requirements are satisfied. We consider the k-anonymity as the privacy requirement. The k-anonymity requirement states that domain values are generalized so that each value of some specified attributes identifies at least k records. The generalization process must not leak more specific information other than the final integrated data. We present a practical and efficient solution to this problem.
In the second part, we study the classification problem involving information spanning multiple private databases. The privacy challenges lie in the facts that data cannot be collected in one place and the classifier itself may disclose private information. We present a novel solution that builds the same decision tree classifier as if data are collected in a central place, but preserves the privacy of participating sites.
BIOGRAPHY:
Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught in the areas of database and data mining. Ke Wang's research interests include database technology, data mining and knowledge discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real life problems.
Enquiries: Miss Temmy So at tel 2609 8444
For more information, please refer to http://www.cse.cuhk.edu.hk/seminar