A Study on Association Rule Hiding Approaches

In recent years, data mining is a popular analysis tool to extract knowledge from collection of large amount of data. One of the great challenges of data mining is finding hidden patterns without revealing sensitive information. Privacy preservation data mining (PPDM) is answer to such challenges. It is a major research area for protecting sensitive data or knowledge while data mining techniques can still be applied efficiently. Association rule hiding is one of the techniques of PPDM to protect the association rules generated by association rule mining. In this paper, we provide a survey of association rule hiding methods for privacy preservation. Various algorithms have been designed for it in recent years. In this paper, we summarize them and survey current existing techniques for association rule hiding.

[1]  Tzung-Pei Hong,et al.  Efficient sanitization of informative association rules , 2008, Expert Syst. Appl..

[2]  Stanley Robson de Medeiros Oliveira,et al.  Privacy preserving frequent itemset mining , 2002 .

[3]  Elisa Bertino,et al.  Association rule hiding , 2004, IEEE Transactions on Knowledge and Data Engineering.

[4]  Yannis Theodoridis,et al.  A quantitative and qualitative ANALYSIS of blocking in association rule hiding , 2004, WPES '04.

[5]  Aris Gkoulalas-Divanis,et al.  Association Rule Hiding for Data Mining , 2010, Advances in Database Systems.

[6]  George V. Moustakides,et al.  A Max-Min Approach for Hiding Frequent Itemsets , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[7]  Yücel Saygin,et al.  Privacy preserving association rule mining , 2002, Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002.

[8]  Aris Gkoulalas-Divanis,et al.  An integer programming approach for frequent itemset hiding , 2006, CIKM '06.

[9]  Shyue-Liang Wang,et al.  Hiding informative association rule sets , 2007, Expert Syst. Appl..

[10]  Philip S. Yu,et al.  A border-based approach for hiding sensitive frequent itemsets , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[11]  Shan-Tai Chen,et al.  A Novel Algorithm for Completely Hiding Sensitive Association Rules , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[12]  Vassilios S. Verykios,et al.  Disclosure limitation of sensitive rules , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[13]  Philip S. Yu,et al.  Hiding Sensitive Frequent Itemsets by a Border-Based Approach , 2007, J. Comput. Sci. Eng..

[14]  Aris Gkoulalas-Divanis,et al.  Exact Knowledge Hiding through Database Extension , 2009, IEEE Transactions on Knowledge and Data Engineering.

[15]  George V. Moustakides,et al.  A MaxMin approach for hiding frequent itemsets , 2008, Data Knowl. Eng..

[16]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[17]  Chris Clifton,et al.  Using unknowns to prevent discovery of association rules , 2001, SGMD.

[18]  Dhiren R. Patel,et al.  Maintaining privacy and data quality in privacy preserving association rule mining , 2010, 2010 Second International conference on Computing, Communication and Networking Technologies.

[19]  Tzung-Pei Hong,et al.  Hiding collaborative recommendation association rules , 2007, Applied Intelligence.