Master’s Thesis Presentation • Cryptography, Security, and Privacy (CrySP) • On Using Embeddings for Ownership Verification of Graph Neural Networks

Please note: This master’s thesis presentation will take place online.

Asim Waheed, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor N. Asokan

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a GNN is to learn an embedding for each graph node in a dataset that encodes both the node features and the local graph structure around the node. Embeddings generated by a GNN for a graph node are unique to that GNN.

Prior work has shown that GNNs are prone to model extraction attacks. Model extraction attacks and defenses have been explored extensively in other non-graph settings. While detecting or preventing model extraction appears to be difficult, deterring them via effective ownership verification techniques offers a potential defense. In non-graph settings, fingerprinting models, or the data used to build them, have shown to be a promising approach toward ownership verification.

We hypothesize that the embeddings generated by a GNN are useful for fingerprints. Based on this hypothesis, we present GrOVe, a state-of-the-art GNN model fingerprinting scheme that, given a target model and a suspect model, can reliably determine if the suspect model was trained independently of the target model or if it is a surrogate of the target model obtained via model extraction. We show that GrOVe can distinguish between surrogate and independent models even when the independent model uses the same training dataset and architecture as the original target model.

Using six benchmark datasets and three model architectures, we show that GrOVe consistently achieves low false-positive and false-negative rates. We demonstrate that GrOVe is robust against known fingerprint evasion techniques while remaining computationally efficient.

To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/98315162061.