Privacy leakage in multi-relational databases via pattern based semi-supervised learning

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2005, Pages 355-356

  Xiong, Hui ^a   Steinbach, Michael ^b   Kumar, Vipin ^b  

^a Rutgers University   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Database Security; Hyperclique Patterns; Privacy Preserving Data Mining; Semi supervised Learning

Indexed keywords

DATA MINING; DATA PRIVACY; LEARNING SYSTEMS; PATTERN RECOGNITION; SECURITY OF DATA; SUPERVISORY AND EXECUTIVE PROGRAMS;

DATABASE SECURITY; HYPERCLIQUE PATTERNS; PRIVACY PRESERVING DATA MINING; SEMI-SUPERVISED LEARNING;

DATABASE SYSTEMS;

EID: 33745793065     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1099554.1099664     Document Type: Conference Paper

References (7)

1. C. Clifton. Using sample size to limit exposure to data mining. J. Comput. Secur., 8(4):281-307, 2000.
2. P. Domingos. Prospects and challenges for multi- relational data mining. SIGKDD Explorations, 2003.
3. R. Duin. Classifiers in almost empty spaces. In Proc. 15th Int'l Conference on Pattern Recognition, 2000.
4. S. Raudys and A. Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE TPAMI, 13(3):252-264, 1991.
5. M. Seeger. Learning with labeled and unlabeled data. In Technical Report, University of Edinburgh, 2001.
6. H. Xiong, M. Steinbach, and V. Kumar. Privacy leakage in multi-relational databases via pattern based semi-supervised learning. Technical Report 04-023, University of Minnesota, 2004.
7. H. Xiong, P. Tan, and V. Kumar. Mining strong affinity association patterns in data sets with skewed support distribution. In Proc. of ICDM, 2003.