Using unknowns to prevent discovery of association rules

SIGMOD Record

Volumn 30, Issue 4, 2001, Pages 45-54

  Saygin, Yücel ^a   Verykios, Vassilios S ^b   Clifton, Chris ^c  

^a BILKENT UNIVERSITY   (Turkey)

^b DREXEL UNIVERSITY   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0344990562     PISSN: 01635808     EISSN: None     Source Type: Journal    
DOI: 10.1145/604264.604271     Document Type: Article

References (9)

1. D. Agrawal and C. Aggarwal. On the Deisgn and Quantification of Privacy Preserving Data Mining Algorithms. Proceedings of PODS, pages 247-255, 2001.
2. R. Agrawal and R. Srikant. Privacy Preserving Data Mining. Proceedings of SIGMOD Conference, pages 45-52, 2000.
3. M. J. Atallah, E. Bertino, A. K. Elmagarmid, M. Ibrahim, and V. S. Verykios. Disclosure Limitation of Sensitive Rules. Proceedings of IEEE Knolwedge and Data Engineering Workshop, pages 45-52, November 1999.
4. L. Chang and I. S. Moskowitz. Parsimonious Downgrading and Decision Trees Applied to the Inference Problem. Proceedings of the Workshop of New Security Paradigms, pages 82-89, 1999.
5. C. Clifton. Using Sample Size to Limit Exposure to Data Mining. Journal of Computer Security, 8(4), 2000.
6. D. Elena, V. S. Verykios, A. K. Elmagarmid, and E. Bertino. Hiding Association Rules by using Confidence and Support. To appear in the Proceedings of Information Hiding Workshop, 2001.
7. T. H. Hinke, H. S. Delugach, and R. P. Wolf. Protecting databases from inference attacks. Computers and Security, 16(8):687-708, 1997.
8. U. of California at Irvine Machine Learning Repository. http://www.ics.uci.edu/∼mlearn/MLSummary.html.
9. V. S. Verykios, A. K. Elmagarmid, B. Elisa, D. Elena, and Y. Saygin. Association Rule Hiding. IEEE Transactions on Knowledge and Data Engineering, 2000. Under review.