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Found 4 papers, 2 papers with code
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
This work addresses this gap by introducing differentially private active learning (DP-AL) for standard learning settings.
Visual Privacy Auditing with Diffusion Models
Data reconstruction attacks on machine learning models pose a substantial threat to privacy, potentially leaking sensitive information.
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.
Fully Hyperbolic Convolutional Neural Networks for Computer Vision
To address this, we present HCNN, a fully hyperbolic convolutional neural network (CNN) designed for computer vision tasks.
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