ABSTRACT:

Data mining technology identifies patterns and trends from large collections of published data. Including personal information in published data is necessary for data mining, but may violate the privacy of individuals. So it is crucial to study how to preserve privacy in data mining.

In this talk, we give an overview of recent work on anonymized data publishing for privacy preservation data mining. In the first part, we introduce the k-anonymity problem, together with its complexity, (approximation) algorithms, and how to apply it in data mining. Several extensions to k-anonymity are also discussed. In the second part, we discuss the tradeoff between the utility of the published data and the loss of the privacy. In the third part, we show that anonymized data publishing is vulnerable to composition attacks, and a possible solution.

BIO:

Bolin Ding is a Ph.D. candidate in the Department of Computer Science, University of Illinois at Urbana-Champaign. He is interested in data mining, databases, and algorithm design and analysis. He got his MPhil's degree on System Engineering from the Chinese University of Hong Kong in 2007, and a Bachelor's degree in Math and Applied Mathematics from Renmin University of China in 2005.