Integrating multiple online social networks (OSNs) has important implications
for many downstream social mining tasks, such as user preference modelling,
recommendation, and link prediction. However, it is unfortunately accompanied
by growing privacy concerns about leaking sensitive user information. How to
fully utilize the data from different online social networks while preserving
user privacy remains largely unsolved. To this end, we propose a Cross-network
Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive
representations of users in a privacy-preserving way. We jointly consider
information from partially aligned social networks with differential privacy
guarantees. In particular, for each heterogeneous social network, we first
introduce a hybrid differential privacy notion to capture the variation of
privacy expectations for heterogeneous data types. Next, to find user linkages
across social networks, we make unsupervised user embedding-based alignment in
which the user embeddings are achieved by the heterogeneous network embedding
technology. To further enhance user embeddings, a novel cross-network GCN
embedding model is designed to transfer knowledge across networks through those
aligned users. Extensive experiments on three real-world datasets demonstrate
that our approach makes a significant improvement on user interest prediction
tasks as well as defending user attribute inference attacks from embedding.

提出一个跨网络社交用户嵌入框架 DP-CroSUE，以隐私保护的方式学习用户的全面表示，并在三个真实数据集上展示对用户兴趣预测任务的显著改进和防御嵌入式用户属性推断攻击。