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.