An Efficient Algorithm for Privacy Preserving Data Mining Using Heuristic Approach

Privacy preserving data mining is an important topic on which lot of researchers going on last years. There are many approaches to hide association rule. In this paper Efficient Heuristic approach method is proposed which is more effective to hide association rule. The objective of this algorithm is to extract relevant knowledge from large amount of data, while protecting at the time sensitive information. The proposed method focused on hiding set of frequent items containing highly sensitive knowledge that only remove information from transactional database with no hiding failure.

[1]  Brian C.S. Loh,et al.  Ontology-Enhanced Interactive Anonymization in Domain-Driven Data Mining Outsourcing , 2010, 2010 Second International Symposium on Data, Privacy, and E-Commerce.

[2]  Guang Li,et al.  Privacy-Preserving Data Mining Based on Sample Selection and Singular Value Decomposition , 2011, 2011 International Conference on Internet Computing and Information Services.

[3]  Ljiljana Brankovic,et al.  Noise Addition for Protecting Privacy in Data Mining , 2003 .

[4]  Yongdae Kim,et al.  Efficient Cryptographic Primitives for Private Data Mining , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[5]  Jun Zhang,et al.  Combined data distortion strategies for privacy-preserving data mining , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[6]  K. Srinivasa Rao,et al.  Distortion Based Algorithms For Privacy Preserving Frequent Item Set Mining , 2011 .

[7]  Elisa Bertino,et al.  Privacy-Preserving Updates to Anonymous and Confidential Databases , 2011, IEEE Transactions on Dependable and Secure Computing.

[8]  Barış Yıldız,et al.  Hiding Sensitive Predictive Frequent Itemsets , 2011 .

[9]  Vinod Kumar Yadav,et al.  An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining , 2011 .

[10]  Chris Clifton,et al.  Leveraging the "Multi" in secure multi-party computation , 2003, WPES '03.

[11]  Sugumar Rajendran,et al.  Determining the Existence of Quantitative Association Rule Hiding in Privacy Preserving Data Mining , 2012 .

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

[13]  Wenliang Du,et al.  Using randomized response techniques for privacy-preserving data mining , 2003, KDD '03.

[14]  Kyriakos Mouratidis,et al.  Anonymous Query Processing in Road Networks , 2010, IEEE Transactions on Knowledge and Data Engineering.