In this paper, we touch on many areas of research that have been heavily studied. The area of privacy inside a social network encompasses a large breadth, based on how privacy is defined. In authors consider an attack against an anonymized network. In their model, the network consists of only nodes and edges. Trait details are not included. The goal of the attacker is to simply identify people. Further, their problem is very different than the one considered in this paper because they ignore trait details and do not consider the effect of the existence of trait details on privacy. In the authors consider several ways of anonymizing social networks. Our work focuses on inferring details from nodes in the network, not individually identifying individuals. Other papers have tried to infer private information inside social networks. Authors consider ways to infer private information via friendship links by creating a Bayesian Network from the links inside a social network. While they crawl a real social network, Livejournal, they use hypothetical attributes to analyze their learning algorithm. Also, compared to, we provide techniques that can help with choosing the most effective traits or links that need to be removed for protecting privacy. Finally, we explore the effect of collective inference techniques in possible inference attacks