  The rapid expansion of AI in healthcare has led to a surge in medical data
generation and storage, boosting medical AI development. However, fears of
unauthorized use, like training commercial AI models, hinder researchers from
sharing their valuable datasets. To encourage data sharing, one promising
solution is to introduce imperceptible noise into the data. This method aims to
safeguard the data against unauthorized training by inducing degradation in the
generalization ability of the trained model. However, they are not effective
and efficient when applied to medical data, mainly due to the ignorance of the
sparse nature of medical images. To address this problem, we propose the
Sparsity-Aware Local Masking (SALM) method, a novel approach that selectively
perturbs significant pixel regions rather than the entire image as previously.
This simple yet effective approach, by focusing on local areas, significantly
narrows down the search space for disturbances and fully leverages the
characteristics of sparsity. Our extensive experiments across various datasets
and model architectures demonstrate that SALM effectively prevents unauthorized
training of different models and outperforms previous SoTA data protection
methods.
