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                                                     Seminar

             Department of Systems Engineering and Engineering Management
                                  The Chinese University of Hong Kong

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Title	:	Some new issues in privacy preserving data publishing
Speaker	:	Prof. Ke Wang
		School of Computing Science,
		Simon Fraser University.
Date	:	June 5th, 2009 (Friday)
Time	:	4:30 p.m. - 5:30 p.m.
Venue	:	Room 513
		William M.W. Mong Engineering Building
		(Engineering Building Complex Phase 2)
		CUHK

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Abstract:
　

This talk examines two topics in privacy preservation. The first topic considers the problem of publishing \"transaction data\" for research purposes. Each transaction is an arbitrary set of items chosen from a large universe. Detailed transaction data provides an electronic image of one\'s life. This has two implications. One, transaction data are excellent candidates for data mining research. Two, use of transaction data would raise serious concerns over individual privacy. Therefore, before transaction data is released for data mining, it must be made anonymous so that data subjects cannot be re-identified. The challenge is that transaction data has no structure and can be extremely high dimensional. Traditional anonymization methods lose too much information on such data. To date, there has been no satisfactory privacy notion and solution proposed for anonymizing transaction data. This paper proposes one way to address this issue.

In the second topic, I will examine the universal assumption on quasi-identifiers and sensitive attributes in the privacy literature. We present an analysis showing this assumption fails to address some practical cases. We present an alternative.

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Biography:
　

Ke Wang is currently a professor at School of Computing Science, Simon Fraser University. His 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 and user utility (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 database, statistics, machine learning and optimization to provide actionable solutions to real life problems. Ke Wang has published in database, information retrieval, and data mining conferences, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM and ICDM. He was an associate editor of the IEEE TKDE journal, an editorial board member for Journal of Data Mining and Knowledge Discovery, and the PC co-chair for SIAM Conference on Data Mining 2008.

************************* ALL ARE WELCOME ************************

Host	:	Prof. Cheng, Hong
Tel	:	(852) 2609-8300
Email	:	hcheng@se.cuhk.edu.hk
Enquiries	:	Prof. Nan Chen or Prof. Sean X. Zhou
	:	Department of Systems Engineering and Engineering Management
		CUHK
Website	:	http://www.se.cuhk.edu.hk/~seg5810
Email	:	seg5810@se.cuhk.edu.hk

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