Protecting sensitive knowledge by data sanitization

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2003, Pages 613-616

  Oliveira, Stanley R M ^a,b   Zaïane, Osmar R ^b  

^a EMBRAPA INFORMÁTICA AGROPECUÁRIA   (Brazil)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33746056957     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V. Verykios. Disclosure Limitation of Sensitive Rules. In Proc. of IEEE Knowledge and Data Engineering Workshop, pages 45-52, Chicago, Illinois, November 1999.
2. E. Dasseni, V. S. Verykios, A. K. Elmagarmid, and E. Bertino. Hiding Association Rules by Using Confidence and Support. In Proc. of the 4th Information Hiding Workshop, pages 369-383, Pittsburg, PA, April 2001.
3. S. R. M. Oliveira and O. R. Zaïane. Privacy Preserving Frequent Itemset Mining. In Proc. of the IEEE ICDM Workshop on Privacy, Security, and Data Mining, pages 43-54, Maebashi City, Japan, December 2002.
4. S. R. M. Oliveira and O. R. Zaïane. Algorithms for Balancing Privacy and Knowledge Discovery in Association Rule Mining. In Proc. of the 7th International Database Engineering and Applications Symposium (IDEAS'03), Hong Kong, China, July 2003.
5. S. R. M. Oliveira and O. R. Zaïane. An Efficient One-Scan Sanitization For Improving The Balance Between Privacy And Knowledge Discovery. Technical report, TR03-15, Computer Science Department, University of Alberta, Canada, June 2003.
6. Y. Saygin, V. S. Verykios, and C. Clifton. Using Unknowns to Prevent Discovery of Association Rules. SIGMOD Record, 30(4):45-54, December 2001. (Pubitemid 33720998)