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Found 4 papers, 2 papers with code
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging.
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
This work addresses these challenges exploring and quantifying the utility of privacy-preserving deep learning techniques, concretely, (i) differentially private stochastic gradient descent (DP-SGD) and (ii) fully synthetic training data generated by our proposed malignancy-conditioned generative adversarial network.
Bounding Reconstruction Attack Success of Adversaries Without Data Priors
When training ML models with differential privacy (DP), formal upper bounds on the success of such reconstruction attacks can be provided.
Complex-valued Federated Learning with Differential Privacy and MRI Applications
Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task (MRI pulse sequence classification in $k$-space), yielding excellent utility and privacy.
