PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep
  Intellectual Property Protection
Enyan Dai Minhua Lin Suhang Wang
Pretraining on Graph Neural Networks (GNNs) has shown great power in
facilitating various downstream tasks. As pretraining generally requires huge
amount of data and computational resources, the pretrained GNNs are high-value
Intellectual Properties (IP) of the legitimate owner. However, adversaries may
illegally copy and deploy the pretrained GNN models for their downstream tasks.
Though initial efforts have been made to watermark GNN classifiers for IP
protection, these methods require the target classification task for
watermarking, and thus are not applicable to self-supervised pretraining of GNN
models. Hence, in this work, we propose a novel framework named PreGIP to
watermark the pretraining of GNN encoder for IP protection while maintain the
high-quality of the embedding space. PreGIP incorporates a task-free
watermarking loss to watermark the embedding space of pretrained GNN encoder. A
finetuning-resistant watermark injection is further deployed. Theoretical
analysis and extensive experiments show the effectiveness of {\method} in IP
protection and maintaining high-performance for downstream tasks.