A Data Ownership Authentication Method for Graph Neural Networks via Clean-Label Backdoor

Graph Neural Network(GNN) have gained extensive adoption in diverse fields, including IoT anomaly detection, social network analysis, and drug molecule prediction, due to their exceptional ability to handle graph-structured data. However, as GNN models become more widely used, the issue of dataset leakage has become increasingly prominent, posing significant risks to the rights and interests of data owners. In this paper, we propose a data ownership authentication method based on GNN backdoor watermarking, termed Graph Data Ownership Authentication(GDOA). Specifically, the data owner injects covert backdoor watermark triggers into some of the samples in the dataset. When an unauthorized user uses the data to train their own model, the model will be injected with the backdoor. The data owner can then validate the target model using samples with the same triggers to determine whether the target model was trained using the unauthorized dataset. GDOA utilizes the clean-label backdoor method to achieve ownership authentication of the dataset due to the greater stealthiness of the clean-label backdoor. Specifically, the watermark samples are selected within the target class, and the samples’ own labels are used as target labels, effectively avoiding the issue of label confusion. In addition, to improve the authentication success rate, GDOA optimizes the injection position of feature triggers in the feature vector. Our extensive experiments across multiple models and benchmark datasets demonstrate that GDOA achieves an average authentication success rate of over 90%, validating its effectiveness for graph data ownership verification.