A Review on l-diversity with clustering in privacy preserving
Keywords:Data mining; l-diversity; privacy preserving; k-anonymity; clustering
detailed personal data is regularly collected and sharing in very huge amount. All of these data are useful
for data mining application. These all data include shopping habits, criminal records, medical history, credit records
etc. On one side such information is a very useful resource for business association and governments for choice
analyzing. On the other side privacy regulations and other privacy concerns may prevent data owners from sharing
information for data analysis. Two basic handling systems used to accomplish anonymization of a data set are
generalization and suppression. Generalization refers to replacing a value with a less particular however semantically
predictable value, while suppression refers to not releasing a value at all. This generalizes and suppression based
Anonymity is good choice to hide personal information from the attackers but it also suffers from information loss. In
propose method here use clustering approach to the l-diverse anonymity, this algorithm first clustering instances
based on sensitive attribute and then arrange them with less information loss and as per the criteria of l-diversity.
With this approach information loss is minimized. Proposed algorithm makes this approach efficient.