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Research Paper | Computer Science & Engineering | India | Volume 3 Issue 10, October 2014
A Well-Organized Method for Protecting Sensitive Information
Priyanka Choubey | Savita Rathoad
Abstract: Data mining services need correct input data for their results to be significant, but privacy concerns may power users to offer spurious information. To protect client privacy in the data mining process, a multiplicity of techniques based on random perturbation of data records have been proposed lately. One known fact which is very significant in data mining is discovering the association rules from database of transactions each transaction consists of set of objects. Two important terms support and confidence are linked with each of the association rule. Actually any rule is called as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes we do not want to disclose sensitive rules to the public because of privacy customized definition of support and confidence so that it would hide any desired sensitive association regulation without any side effect. in reality the enhanced technique is by means of the same method (as previously used method) of receiving association rules but modified definitions of support and confidence are used.
Keywords: purposes There are many approaches to hide confident association rules which take the support and confidence as a base for algorithms and many more The proposed work has the basis of reduction of support and confidence of sensitive rules but this work is not restriction or disturbing the given database of transactions directlyThe proposed algorithm uses some Association rules Support, Confidence, Data mining, Data hiding, etc
Edition: Volume 3 Issue 10, October 2014,
Pages: 1602 - 1605
How to Cite this Article?
Priyanka Choubey, Savita Rathoad, "A Well-Organized Method for Protecting Sensitive Information", International Journal of Science and Research (IJSR), Volume 3 Issue 10, October 2014, pp. 1602-1605, https://www.ijsr.net/get_abstract.php?paper_id=2015166
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