Anchor Link Prediction for Privacy Leakage via De-Anonymization in Multiple Social Networks

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces...

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Bibliographic Details
Published in:IEEE transactions on dependable and secure computing 2023-11, Vol.20 (6), p.1-17
Main Authors: Wang, Huanran, Yang, Wu, Man, Dapeng, Wang, Wei, Lv, Jiguang
Format: Article
Language:English
Subjects:
Adversarial machine learning
Anchor link prediction
Blogs
De-anonymization
Embedding
Federated adversarial learning
Graph embedding
Leakage
Learning
Network analysis
Nodes
Predictive models
Privacy
Privacy leakage
Semantics
Servers
Social network
Social networking (online)
Social networks
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Summary:Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this paper, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2023.3242009