Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
Date
2023
Authors
Zhang, H.
Wu, B.
Wang, S.
Yang, X.
Xue, M.
Pan, S.
Yuan, X.
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Advisors
Journal Title
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Conference paper
Citation
Proceedings of the 40th International Conference on Machine Learning (ICML'23), as published in Proceedings of Machine Learning Research, 2023, vol.202, pp.41737-41752
Statement of Responsibility
He Zhang, BangWu, ShuoWang, Xiangwen Yang, Minhui Xue, Shirui Pan, Xingliang Yuan
Conference Name
40th International Conference on Machine Learning (ICML) (23 Jul 2023 - 29 Jul 2023 : Honolulu, Hawaii, USA)
Abstract
While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in realworld applications, they have been shown to be vulnerable to a growing number of privacy attacks. For instance, link stealing is a well-known membership inference attack (MIA) on edges that infers the presence of an edge in a GNN’s training graph. Recent studies on independent and identically distributed data (e.g., images) have empirically demonstrated that individuals from different groups suffer from different levels of privacy risks to MIAs, i.e., uneven vulnerability. However, theoretical evidence for such uneven vulnerability is missing. In this paper, we first present theoretical evidence of the uneven vulnerability of GNNs to link stealing attacks, which lays the foundation for demystifying such uneven risks among different groups of edges. We further demonstrate a group-based attack paradigm to expose the practical privacy harm to GNN users derived from the uneven vulnerability of edges. Finally, we empirically validate the existence of obvious uneven vulnerability on ten real-world datasets (e.g., about 25% AUC difference between different groups in the Credit graph). Compared with existing methods, the outperformance of our group-based attack paradigm confirms that customising different strategies for different groups results in more effective privacy attacks.
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© Copyright 2023 by the author(s).