Related papers: GENIE: Watermarking Graph Neural Networks for Link Prediction

GENIE: Watermarking Graph Neural Networks for Link Prediction

URL: http://arxiv.org/abs/2406.04805v1
Date: Fri, 7 Jun 2024 10:12:01 GMT
Title: GENIE: Watermarking Graph Neural Networks for Link Prediction
Authors: Venkata Sai Pranav Bachina, Ankit Gangwal, Aaryan Ajay Sharma, Charu Sharma,
Abstract summary: Graph Neural Networks (GNNs) have advanced the field of machine learning by utilizing graph-structured data. Recent studies have shown GNNs to be vulnerable to model-stealing attacks. Watermarking has been shown to be effective at protecting the IP of a GNN model.
Score: 5.1323099412421636
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Graph Neural Networks (GNNs) have advanced the field of machine learning by utilizing graph-structured data, which is ubiquitous in the real world. GNNs have applications in various fields, ranging from social network analysis to drug discovery. GNN training is strenuous, requiring significant computational resources and human expertise. It makes a trained GNN an indispensable Intellectual Property (IP) for its owner. Recent studies have shown GNNs to be vulnerable to model-stealing attacks, which raises concerns over IP rights protection. Watermarking has been shown to be effective at protecting the IP of a GNN model. Existing efforts to develop a watermarking scheme for GNNs have only focused on the node classification and the graph classification tasks. To the best of our knowledge, we introduce the first-ever watermarking scheme for GNNs tailored to the Link Prediction (LP) task. We call our proposed watermarking scheme GENIE (watermarking Graph nEural Networks for lInk prEdiction). We design GENIE using a novel backdoor attack to create a trigger set for two key methods of LP: (1) node representation-based and (2) subgraph-based. In GENIE, the watermark is embedded into the GNN model by training it on both the trigger set and a modified training set, resulting in a watermarked GNN model. To assess a suspect model, we verify the watermark against the trigger set. We extensively evaluate GENIE across 3 model architectures (i.e., SEAL, GCN, and GraphSAGE) and 7 real-world datasets. Furthermore, we validate the robustness of GENIE against 11 state-of-the-art watermark removal techniques and 3 model extraction attacks. We also demonstrate that GENIE is robust against ownership piracy attack. Our ownership demonstration scheme statistically guarantees both False Positive Rate (FPR) and False Negative Rate (FNR) to be less than $10^{-6}$.

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