Prevention of inference attacks for private information in social networking sites

One of the most important terms we hear these days is Social networks which are online applications which allow its users to connect by various link types. 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. Online social networks, such as Facebook, LinkedIn are progressively utilized by many people. It makes digital communication technologies sharpening tools for extending the social circle of people. It has already become an important integral part of our daily lives, enabling us to contact our friends and families on time. As social networks have developed rapidly, recent research has begun to explore social networks to understand their structure, advertising and marketing, and data mining online population, representing 1.2 billion users around the world. More and more social network data has been made publicly available and analyzed in one way or another. But some of the information revealed is meant to be private hence social network data has led to the risk of leakage of confidential information of individuals. This is because they collect huge personal data and users take risks of trusting them. It is possible to use learning algorithms on released data to predict private information. Privacy preservation is a significant research issue in social networking. Since more personalized information is shared with the public, violating the privacy of a target user become much easier. 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. We explore how to launch inference attacks using released social networking data to predict private information. This paper also comes with the security features which are essential for an online social networking site. The research in this field is still in its early years. We argue that the different privacy problems are entangled and that research on privacy in OSNs would benefit from a more holistic approach.