Online social networks are a place where a large community of people publishes details about themselves and their friends which meant to be kept private. Many social networks provide options and security to user for the purpose of keeping their information private. Yet it is possible to predict private information from the released data using a learning algorithm. In this project, we address the inference attacks issues which uses released social networking data to predict private information. We then devise three possible sanitization techniques that could be used in various situations. 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. © Research India Publications.