Deep Learning-Based User Privacy Settings Recommendation in Online Social Networks

Privacy concerns have long been a nuisance for users when using online social networks (OSNs), and most OSNs require users to configure the privacy settings themselves. Researchers are paying attention to the recommendation of privacy settings for OSN users, as some studies have found it difficult for users to understand and to use. This paper addresses the problem of recommending user-level privacy settings by leveraging the user-generated content (UGC) sequences and account information. We first analyze the differences of users with different privacy settings using the crawled Twitter dataset. Further, we formalize the privacy settings recommendation problem as a multi-label classification task and propose a deep learning-based privacy settings recommendation approach, PrivacyRec. It adopts Recurrent Neural Networks (RNNs) for the UGC sequences modeling to extract deep representations of users' semantic preferences and interaction behavior. In addition, it fuses the above representations with processed account portrait feature through attention mechanism and infers the privacy settings. We evaluate PrivacyRec using two real-world datasets and compare it with several baseline methods. The experimental results demonstrate the effectiveness of PrivacyRec's ability to recommend privacy settings.