Abstract
Data mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. We introduce a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. The efficacy and complexity of this method are discussed. We also present an experiment showing an example of this methodology.
- D. Agrawal and C. Aggarwal. On the Deisgn and Quantification of Privacy Preserving Data Mining Algorithms. Proceedings of PODS, pages 247-255, 2001. Google ScholarDigital Library
- R. Agrawal and R. Srikant. Privacy Preserving Data Mining. Proceedings of SIGMOD Conference, pages 45-52, 2000. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- C. Clifton. Using Sample Size to Limit Exposure to Data Mining. Journal of Computer Security, 8(4), 2000. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- T. H. Hinke, H. S. Delugach, and R. P. Wolf. Protecting databases from inference attacks. Computers and Security, 16(8):687-708, 1997.Google ScholarDigital Library
- U. of California at Irvine Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLSummary.html.Google Scholar
- 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. Google ScholarDigital Library
Index Terms
- Using unknowns to prevent discovery of association rules
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