Semi-supervised policy recommendation for online social networks

Fine-grained policy settings in social networking sites are becoming important for managing user privacy. Incorrect privacy policy settings can easily lead to leaks in private and personal information. At the same time, being too restrictive would reduce the benefits of online social networks. This is further complicated due to the growing adoption of social networks and the rapid growth in information uploading and sharing. The problem of facilitating policy settings has attracted the attention of numerous access control, and human–computer interaction researchers. The proposed solutions range from usable interfaces for policy settings to automated policy settings. We propose a fine-grained policy recommendation system that is based on an iterative semi-supervised learning approach which leverages the social graph propagation properties. Active learning and social graph properties are used to detect the most informative instances to be labeled as training sets. We implemented and tested our approach using both participant-labeled Facebook dataset and their real policy dataset extracted using the Facebook API. We compared our proposed approach to supervised learning and random walk-based approaches. Our approach provided higher accuracy and precision for both datasets. Collaborative active learning further improved the performance of our approach. Moreover, the accuracy and precision of our approach were maintained with the addition of new friends in the social graph.