Providing Security in Social Network with Privacy Preservation

Social network is one of the most important terms we hear these days. Due to its enormous growth, any individual can become a member in any of these online social networking sites. Consequently, these websites gain huge profit just by providing a platform for the users to communicate. However, we have seen both merits and demerits of these online social networking sites. One of these is that they collect huge personal data and users take risks of trusting them. The collection of these data is made easy by using learning algorithms to predict more private data. This paper explains the possibility of various inference attacks by these private data. These attacks can be minimized by sanitization methods that are put forward in this paper. This paper also comes with the security features which are essential for an online social networking site.