Graph Neural Networks (GNNs) have been widely used in various graph-based applications. Recent studies have shown that GNNs are vulnerable to link-level membership inference attacks (LMIA) which can infer whether a given link was included in the training graph of a GNN model. While most of the studies focus on the privacy vulnerability of the links in the entire graph, none have inspected the privacy risk of specific subgroups of links (e.g., links between LGBT users). In this paper, we present the first study of disparity in subgroup vulnerability (DSV) of GNNs against LMIA. First, with extensive empirical evaluation, we demonstrate the existence of non-negligible DSV under various settings of GNN models and input graphs. Second, by both statistical and causal analysis, we identify the difference between three specific graph structural properties of subgroups as one of the underlying reasons for DSV. Among the three properties, the difference between subgroup density has the largest causal effect on DSV. Third, inspired by the causal analysis, we design a new defense mechanism named FairDefense to mitigate DSV while providing protection against LMIA. At a high level, at each iteration of target model training, FairDefense randomizes the membership of edges in the training graph with a given probability, aiming to reduce the gap between the density of different subgroups for DSV mitigation. Our empirical results demonstrate that FairDefense outperforms the existing defense methods in the trade-off between defense and target model accuracy. More importantly, it offers better DSV mitigation.
This content will become publicly available on May 19, 2025
Revisiting Black-box Ownership Verification for Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as powerful tools for processing graph-structured data, enabling applications in various domains. Yet, GNNs are vulnerable to model extraction attacks, imposing risks to intellectual property. To mitigate model extraction attacks, model ownership verification is considered an effective method. However, throughout a series of empirical studies, we found that the existing GNN ownership verification methods either mandate unrealistic conditions or present unsatisfactory accuracy under the most practical settings—the black-box setting where the verifier only requires access to the final output (e.g., posterior probability) of the target model and the suspect model.
Inspired by the studies, we propose a new, black-box GNN ownership verification method that involves local independent models and shadow surrogate models to train a classifier for performing ownership verification. Our method boosts the verification accuracy by exploiting two insights: (1) We consider the overall behaviors of the target model for decision-making, better utilizing its holistic fingerprinting; (2) We enrich the fingerprinting of the target model by masking a subset of features of its training data, injecting extra information to facilitate ownership verification.
To assess the effectiveness of our proposed method, we perform an intensive series of evaluations with 5 popular datasets, 5 mainstream GNN architectures, and 16 different settings. Our method achieves nearly perfect accuracy with a marginal impact on the target model in all cases, significantly outperforming the existing methods and enlarging their practicality. We also demonstrate that our method maintains robustness
against adversarial attempts to evade the verification.
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- Award ID(s):
- 2319880
- NSF-PAR ID:
- 10536666
- Publisher / Repository:
- IEEE Symposium on Security and Privacy
- Date Published:
- ISSN:
- 2375-1207
- ISBN:
- 979-8-3503-3130-1
- Page Range / eLocation ID:
- 210-229
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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