Medical Unlearnable Examples: Securing Medical Data from Unauthorized
  Traning via Sparsity-Aware Local Masking
Weixiang Sun Yixin Liu Zhiling Yan Kaidi Xu Lichao Sun
With the rapid growth of artificial intelligence (AI) in healthcare, there
has been a significant increase in the generation and storage of sensitive
medical data. This abundance of data, in turn, has propelled the advancement of
medical AI technologies. However, concerns about unauthorized data
exploitation, such as training commercial AI models, often deter researchers
from making their invaluable datasets publicly available. In response to the
need to protect this hard-to-collect data while still encouraging medical
institutions to share it, 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 model generalization. Although
existing methods have shown commendable data protection capabilities in general
domains, they tend to fall short when applied to biomedical data, mainly due to
their failure to account for 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 previous strategies have done. This simple-yet-effective
approach significantly reduces the perturbation search space by concentrating
on local regions, thereby improving both the efficiency and effectiveness of
data protection for biomedical datasets characterized by sparse features.
Besides, we have demonstrated that SALM maintains the essential characteristics
of the data, ensuring its clinical utility remains uncompromised. Our extensive
experiments across various datasets and model architectures demonstrate that
SALM effectively prevents unauthorized training of deep-learning models and
outperforms previous state-of-the-art data protection methods.