Anchor Link Prediction via Network Structural Role for Privacy Leakage in Edge Computing

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization in edge computing. The predictive effect of traditional unsupervised learning methods is too dependent on user attributes and supervised learning methods are sensitive to network structure noise. Anchor link prediction methods based on graph embedding are restricted by the sparsity of the observable anchor links which can be used for training. To facilitate the effectiveness and robustness of the anchor link prediction, we have proposed a novel method which reduces the restrictions on the observable anchor links used for training. The proposed method consists of two phases. First, graph embedding based on network structural roles is used to generate the latent feature space, reconciling the distinction and similarity between nodes. Second, the supervised learning for optimizing the Wasserstein distance which estimates the minimum amount of work to change one distribution into the other. The combination of the reconciled latent feature space and the estimate for the amount of change alleviates the restriction on observable overlapping parts. Extensive experiments on real-life social networks have demonstrated that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.