Privacy as a service in social network communications

With dispersing of information on social networks - both personally identifiable and general - comes the risk of these information falling into wrong hands. Users are burdened with setting privacy of multiple social networks, each with growing number of privacy settings. Exponential growth of applications (App) running on social networks have made privacy control increasingly difficult. This necessitates Privacy as a service model, especially for social networks, to handle privacy across multiple applications and platforms. Privacy aware information dispersal involves knowing who is receiving what information of ours. Our proposed service employs a supervised learning model to assist user in spotting unintended audience for a post. Different from previous work, we combine both Tie-strength and Context of the information as features in learning. Our evaluation using several classification techniques shows that the proposed method is effective and better than methods using either only Tie-strength or only Context of the information for classification.