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
Association Rule Hiding
Data Mining
Association Rules
Hide Sensitive Rules
 Changed
 Database
User
 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.
State University of New York, Stony Brook
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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)
State University of New York, Stony Brook
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More Association Rule Hiding (Papers)
 [Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes
Gehrke. Privacy Preserving Mining of Association Rules. SIGKDD 2002, Edmonton,
Alberta Canada.
 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).
 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)
 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.
 Yucel Saygin, Vassilios Verykios, and Chris Clifton, Using Unknowns to Prevent
Discovery of Association Rules, SIGMOD Record 30 (2001), no. 4, 45–54.
 S. Verykios, Ahmed K. Elmagarmid, Bertino Elisa, Yucel Saygin, and Dasseni
Elena, Association Rule Hiding, IEEE Transactions on Knowledge and Data
Engineering (2003).
State University of New York, Stony Brook
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Thanks for Your Patience
State University of New York, Stony Brook
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