A Review of Anonymization Approach for Privacy Preservation Data Mining

Journal: GRENZE International Journal of Computer Theory and Engineering
Authors: Ratandeep Kaur, Manisha Sharma, S.Taruna
Volume: 3 Issue: 3
Grenze ID: 01.GIJCTE.3.3.503 Pages: 14-19

Abstract

Social Networking sites have gained enormous popularity over the last few years. Today, internet has become an inevitable part of the lives of more than millions of people. Social networking provides the platform to share the personal information, which has raised the serious concerns related to the privacy and security of the users. A broad area of data mining is focusing on providing the privacy and introduced a field known as privacy preserving data mining(PPDM).This paper(or work) addresses the problem by presenting analysis of anonymization algorithms of privacy preserving data mining (PPDM) such as kanonymity and l-diversity and t-closeness.

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