1. CSE 634 Data Mining Techniques Association Rules Hiding (Not Mining) Prateek Duble (105301354 ) Course Instructor: Prof. Anita Wasilewska State University of New York, Stony Brook

2. 2 Association Rule Hiding New & Data Privacy related Concept Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. User Changed Database Data Mining Association Rules Hide Sensitive Rules

3. State University of New York, Stony Brook3 Association Rule Hiding There are various algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. Association Rule Hiding Techniques  Blocking-based Technique (Saygin, Verykios, Clifton)  Distortion-based (Sanitization) Technique – (Oliveira, Zaiane, Verykios, Dasseni)

4. State University of New York, Stony Brook4 More Association Rule Hiding (Papers) Privacy Preserving Mining of Association Rules. [Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes Gehrke. Privacy Preserving Mining of Association Rules. SIGKDD 2002, Edmonton, Alberta Canada. Privacy Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data Murat Kantarcioglou and Chris Clifton, Privacy Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data, In Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2002). Privacy Preserving Association Rule Mining in Vertically Partitioned Data Jaideep Vaidya and Chris Clifton, Privacy Preserving Association Rule Mining in Vertically Partitioned Data, In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002) Algorithms for Balacing Privacy and Knowledge Discovery in Association Rule Mining Stanley R. M. Oliveira and Osmar R. Zaïane. Algorithms for Balacing Privacy and Knowledge Discovery in Association Rule Mining. In Proc. of the Seventh International Database Engineering & Applications Symposium (IDEAS'03), pp. 54-63, Hong Kong, July 16-18, 2003. Using Unknowns to Prevent Discovery of Association Rules Yucel Saygin, Vassilios Verykios, and Chris Clifton, Using Unknowns to Prevent Discovery of Association Rules, SIGMOD Record 30 (2001), no. 4, 45–54. Association Rule Hiding S. Verykios, Ahmed K. Elmagarmid, Bertino Elisa, Yucel Saygin, and Dasseni Elena, Association Rule Hiding, IEEE Transactions on Knowledge and Data Engineering (2003).

5. State University of New York, Stony Brook5 Thanks for Your Patience