Privacy Preserving Data Mining Model for the Social Networking

In recent years, the social networking sites are becoming the most popular and easiest way to connect with one another. There is a remarkable increase in the usage of social networking sites which provides various services including e-mail, instant message sharing, news groups, blogging, chat groups, and the latest trend of instagrams, snapchats. These services are provided by some popular sites like Facebook, MySpace, YouTube, Twitter as they are the most visited online sites. The rapid growth of social networking sites is resulting as major sources of the database related to numerous amounts of users and lead to privacy and security issues. A broad area of data mining is focused on providing privacy and has introduced a field known as privacy preserving data mining(PPDM). This technique includes various approaches such as generalization, suppression, randomization, anonymization and cryptography. In order to provide insight to the user about their privacy a model has been propose in this paper. The proposed model has used the machine learning algorithms based on classifications such as Decision tree (DT), K-Nearest Neighbour, Naive Bayes.