ONINE SOCIAL NETWORK BASED A NOVEL APPROACH FOR PROTECTING THE USER WALLS

Abstract

Online social networks, such as Face book, 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 thesis explore how to launch inference attacks using released social networking data to predict private information. Three possible sanitization techniques that could be used in various situations. The effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. That can be decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods.