Tracking the Leaker: An Encodable Watermarking Method for Dataset Intellectual Property Protection

Presently, numerous enterprises provide machine learning cloud services. However, the service provider may exploit user-uploaded data for unauthorized model retraining or illicit collection of user data for commercial model development. This study introduces a traceable dataset watermarking technique designed to ascertain the trustworthiness of third-party providers offering machine learning cloud services. In the event of a data breach, the source can be traced back to the suspicious third-party responsible for data leakage. Specifically, we propose a method that employs the clean-label backdoor attack framework to infer whether a third-party model is trained using user data. A watermark, associated with the encoding and designed as a trigger, is injected into the dataset through a trained autoencoder. Experimental evaluation on three datasets proves the effectiveness of the proposed method, yielding over 93% accuracy on average under normal conditions. A series of pruning and fine-tuning attacks were carried out on the method, with the results indicating that these attacks have a minimal impact and confirming the method's robustness.