Sanitization Techniques against Personal Information Inference Attack on Social Network?

Online social networking has become one of the most popular activities on the web. Online social networks such as Facebook are increasingly utilized by many people. OSNs allow users to control and customize what personal information is available to other users. 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. A privacy breach occurs when sensitive information about the user the information that an individual wants to keep from public is disclosed to an adversary. Yet it is possible to use learning algorithms on released data to predict private information. Private information leakage could be an important issue in some cases. Here the goal is simulate the inference attacks using released social networking data to predict private information. In the proposed system desired use of data and individual privacy presents an opportunity for privacy preserving social network data mining. Here in the system there are three possible sanitization techniques that could be used in various situations for preventing inference attack, those techniques are removing details, adding some new information and manipulate some field these techniques are used for preventing inference attack.