CSE 634 Data Mining Techniques

1
CSE 634Data Mining Techniques

• Association Rules Hiding (Not Mining)
• Prateek Duble
• (105301354)
• Course Instructor Prof. Anita Wasilewska
• State University of New York, Stony Brook

2
Association Rule Hiding

• Changed
• Database

• 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.

3
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
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, 4554.
• S. Verykios, Ahmed K. Elmagarmid, Bertino Elisa,
  Yucel Saygin, and Dasseni Elena, Association Rule
  Hiding, IEEE Transactions on Knowledge and Data
  Engineering (2003).

5
Thanks for Your Patience