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A Fuzzy Approach for Privacy Preserving in Data Mining

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 57 - Number 18
Year of Publication: 2012
M. Sridhar
B. Raveendra Babu

M Sridhar and Raveendra B Babu. Article: A Fuzzy Approach for Privacy Preserving in Data Mining. International Journal of Computer Applications 57(18):1-5, November 2012. Full text available. BibTeX

	author = {M. Sridhar and B. Raveendra Babu},
	title = {Article: A Fuzzy Approach for Privacy Preserving in Data Mining},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {57},
	number = {18},
	pages = {1-5},
	month = {November},
	note = {Full text available}


Advances in hardware technology have increased storage and recording capabilities regarding individual's personal data. Privacy preserving of data has to ensure that individual data publishing will refrain from disclosing sensitive data. Data is anonymized to address the data misuse concerns. Recent techniques have highlighted data mining in ways to ensure privacy. Most anonymization techniques are taken from various fields like data mining, cryptography and information hiding. K-Anonymity is a popular approach where data is transformed to equivalence classes and each class has a set of K- records indistinguishable from each other. But there were many problems with this approach and remedies like l-diversity and t-closeness were proposed to overcome them. This paper addresses the problem of Privacy Preserving in Data Mining by transforming the attributes to fuzzy attributes. Due to fuzzification, exact value cannot be predicted thus maintaining individual privacy, and also better accuracy of mining results were achieved.


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