Department of Systems Engineering and Engineering Management

                                 The Chinese University of Hong Kong




Title:  Privacy-Preserving Collaborative Data Mining


Speaker:  Dr. Justin Zhan

                Carnegie Mellon University

Date     :   December 20th, 2006 (Wednesday)

Time    :   4:30p.m. - 5:30p.m.

Venue  :   Room 513

                MMW Engineering Building(Engineering Building Complex Phase 2)







Data mining is a process to extract useful knowledge from large

amounts of data. To conduct data mining, we often need to collect

data from various resources. However, the data are sometimes

distributed among and owned by different parties. Privacy concerns

may prevent the parties from directly sharing the actual values of

data and some types of information about the data. How multiple

parties can collaboratively conduct data mining without breaching

data privacy presents a grand challenge. Theoretical results from

the area of secure multi-party computation show that one may provide

secure protocols for any multi-party computation with honest majority.

However, the general methods are far from efficient and practical

for computing complex functions on inputs consisting of large sets

of data. Therefore, to efficiently tackle the problem, formulated as

*Privacy-Preserving Collaborative Data Mining* (PPDM), we need to

develop privacy-conscious solutions with adequate efficiency. Our goal

is to provide efficient solutions to the problem of data sharing

among multiple parties involved in a data mining task, without

disclosing the data between the parties.


We have developed various privacy-oriented protocols for multiple

parties to conduct the desired data mining tasks.  We provide

efficient solutions to obtain accurate data mining results and

minimize private data disclosure.  The solutions are distributed,

i.e., there is no centralized, trusted party having access to

all the data. Instead, we develop secure protocols using homomorphic

encryption and digital envelope techniques to exchange the data

while preserving the data privacy. In this talk, I will introduce

the challenges of PPDM, provide a solution for privacy-preserving

collaborative association rule mining, and sketch future directions

for this research.






Justin Zhan obtained his Bachelor degree from Liaoning University

of Engineering and Technology in 1997, Master degree from Syracuse

University in 2003, and Ph.D. degree in Computer Science from the

University of Ottawa in 2006. He then joined the Carnegie Mellon

University as a faculty member. His research interests include privacy

and security aspects of data mining, privacy and security issues

in bioinformatics, privacy-preserving scientific computing,

privacy-preserving electronic business, artificial intelligence

applied in the information security domain, data mining approaches

for privacy management, and security technologies associated with

compliance and security intelligence. Dr. Zhan has been served as a

committee chair or a committee member for a set of international

conferences and an editorial board member for several international



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


Host       : Professor  Yang, Christopher Chuen Chi

Tel         : (852) 2609-8239

Email     : yang@se.cuhk.edu.hk


Enquiries: Peixiang Zhao or Jeffrey Xu Yu,

                 Department of Systems Engineering and Engineering Management


Website:   http://www.se.cuhk.edu.hk/~seg5810

Email:       seg5810@se.cuhk.edu.hk