On Stealing Graph Neural Network Models
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed queries can be severely limited. In this paper, we demonstrate how an adversary can extract a GNN with very limited interactions with the model. Our approach first enables the adversary to obtain the model backbone without making direct queries to the victim model and then to strategically utilize a fixed query limit to extract the most informative data. The experiments on eight real-world datasets demonstrate the effectiveness of the attack, even under a very restricted query limit and under defense against model extraction in place. Our findings underscore the need for robust defenses against GNN model extraction threats.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Neural NetworkModel extractionSimilar Papers 제목 키워드 기반
Large Language Models for Link Stealing Attacks Against Graph Neural Networks
Graph data contains rich node features and unique edge information, which have been applied across various domains, such as citation networks or recommendation systems. Graph Neural Networks (GNNs) are specialized for ha…
Recommendation SystemsEfficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing of graph-structured data without relying…
Contrastive LearningStealing Links from Graph Neural Networks
Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data contains sensitive information, e.g., so…
Fraud DetectionRecommendation SystemsModel Stealing Attacks Against Inductive Graph Neural Networks
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to build powerful applications. In particular, …
modelHow Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis, leading to growing demands for model transparency. Recent advances in…
Data AugmentationDrug DiscoveryFinancial Analysis