Author : Divya.R, B.Mahesh and R.Ushasree

Online social networks, such as Facebook, are increasingly utilized by many people. These networks allow users to publish details about themselves and to connect to their friends. Some of the information revealed inside these networks is meant to be private. Yet it is possible to use learning algorithms on released data to predict private information. In this paper, we explore how to launch inference attacks using released social networking data to predict private information. Then, we explore the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. We show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described. Data Sanitization is the process of making sensitive information in non-production databases safe for wider visibility

Keywords: Privacy clarity for Formal data, Generalization Module

Article published in International Journal of Current  Engineering  and Technology, Vol.4,No.3 (June- 2014)