Semantics-Enhanced Privacy Recommendation for Social Networking Sites

Abstract

Privacy protection is a vital issue for safe social interactions within social networking sites (SNS). Although SNSs such as MySpace and Facebook allow users to configure their privacy settings, the task is difficult for normal users with hundreds of online friends. In this paper, I propose an intelligent semantics-based privacy configuration system, named SPAC, to automatically recommend privacy settings for SNS users. SPAC learns users’ privacy configuration patterns and make predictions by utilizing machine learning techniques on users’ profiles and privacy setting history. To increase the accuracy of the predicted privacy settings, especially in the context of heterogeneous user profiles, I enhance privacy configuration predictor by integrating it with structured semantic knowledge. This allows SPAC to make inferences based on additional source of knowledge, resulting in improved accuracy of privacy recommendation. Our experimental results have proven the effectiveness of our approach.