Ke Wang
Database, Data Mining

Professor, Computing Science
MSc, Georgia Institute of Technology
PhD, Georgia Institute of Technology

School of Computing Science
Simon Fraser University
8888 University Drive
Burnaby, British Columbia, Canada V5A 1S6
Tel: 7787824667
Fax: 7787823045

Database and Data Mining Laboratory

Recent Software for Download:

  • Temporal Probabilistic Matrix Factorization : A temporal recommender based on matrix factorization. Publication: Chenyi Zhang, Ke Wang, Hongkun Yu, Jianling Sun, En-Peng Lim. Latent Factor Transition for Dynamic Collaborative Filtering SDM 2014 (9 pages)

  • CUT Classification : A clearance threshold based approach to cost sensitive classification. Publication: Ryan McBride, Ke Wang, Wenyuan Li. Classification by CUT: Clearance Under Threshold . IEEE ICDM 2014 conference, December 2014.

  • Top-Down Specialization (TDS 1.0): Generalize a table to satisfy the k-anonymity privacy requirement and preserve information for classification. Pubication: B.C.M. Fung, K. Wang, and P.S. Yu. "Top-Down Specialization for Information and Privacy Preservation", ICDE 2005

  • Frequent Itemset-based Hierarchical Clustering (FIHC 1.0): Construct a document cluster hierarchy from a set of unlabeled documents based on frequent itemsets. Publication: B.C.M. Fung, K. Wang, and M. Ester. "Hierarchical Document Clustering Using Frequent Itemsets", SDM 2003

    Recent publication:

  • Book: B. C. M. Fung, K. Wang, A. W.-C. Fu, and P. S. Yu. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques, Data Mining and Knowledge Discovery Series. 376 pages, Chapman & Hall/CRC, August 2010. ISBN: 9781420091489

  • B. Fung, K. Wang, R. Chen, P. Yu. Privacy-Preserving Data Publishing: A Survey of Recent Developments. ACM Computing Surveys, Vol. 42, Issue No 4, 1-53, June 2010, ACM Press

  • Ryan McBride, Ke Wang, Wenyuan Li. Classification by CUT: Clearance Under Threshold . IEEE ICDM 2014 conference, December 2014.

  • Chenyi Zhang, Jianling Sun, Ke Wang. Information Propagation in Microblog Networks. IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2013)

  • Nigel Medforth, Ke Wang. Privacy Risk In Graph Stream Publishing For Social Network Applications. ICDM 2011.

  • Amin Milani Fard, Ke Wang.An Efficient Clustering Approach to Web Query Ananymization. SECRYPT 2010.

  • R. She, J. Chu, K. Wang, J. Pei, J. Chen. genBlastA: enabling BLAST to identify homologous gene sequences. Genome Research, January 2009 19:143-149; Published in Advance October 6, 2008, doi:10.1101/gr.08 2081.108

  • Y. Xu, K. Wang, A. Fu and P. Yu. Anonymizing Transaction Databases for Publication . SIGKDD 2008

  • Y. Xu, B. Zhang, Z. Chen, and K. Wang. Privacy-enhancing personalized Web search. WWW 2007

  • H. W. Lauw, E.P. Lim, and K. Wang. Summarizing review scores of unequal reviewers. SDM 2007

  • K. Wang, B.C.M. Fung, and P.S. Yu. Handicapping attacker's confidence: an alternative to k-anonymization. Invited paper, Knowledge and Information Systems: An International Journal, 11(3):345-368, April 2007.

  • K. Wang, B.C.M. Fung, and P.S. Yu. Template-based privacy preservation in classification problems. ICDM 2005.

  • K. Wang and B.C.M. Fung. Anonymizing sequential releases. SIGKDD 2006.

  • R.C.W. Wong, J. Li, A.W.C. Fu, and K. Wang. (alpha, k)-Anonymity: an enhanced k-anonymity model for privacy-preserving data publishing. SIGKDD 2006.

  • K. Wang, P.S. Yu, and S. Chakraborty. Bottom-up generalization: a data mining solution to privacy protection. ICDM 2004.









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