A Study on Association Rule Hiding Approaches
Komal Shah1, Asmit Thakkar2, Amit Ganatra3
1Komal Shah, U and P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, India.
2Asmit Thakkar, Department of Information Technology, Chandubhai S Patel Institute of Technology, Changa, India.
3Amit Ganatra, U and P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, India.
Manuscript received on january 17, 2012. | Revised Manuscript received on February 05, 2012. | Manuscript published on February 29, 2012. | PP: 72-76 | Volume-1 Issue-3, February 2012. | Retrieval Number: C0197021312/2011©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Association Rule Hiding, Data Mining, Privacy Preservation Data Mining.