While modern machine learning models rely on increasingly large training datasets, data is
often limited in privacy-sensitive domains. Generative models trained with differential privacy …

The ability to generate privacy-preserving synthetic versions of sensitive image datasets
could unlock numerous ML applications currently constrained by data availability. Due to …

We study the problem of private distribution learning with access to public data. In this setup,
which we refer to as* public-private learning*, the learner is given public and private …

As the prevalence of data analysis grows, safeguarding data privacy has become a
paramount concern. Consequently, there has been an upsurge in the development of …

Automatically discovering failures in vision models under real-world settings remains an
open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and …

Generating differentially private (DP) synthetic data that closely resembles the original
private data is a scalable way to mitigate privacy concerns in the current data-driven world …

Differential Privacy (DP) image data synthesis, which leverages the DP technique to
generate synthetic data to replace the sensitive data, allowing organizations to share and …

Diffusion models (DMs) are one of the most widely used generative models for producing
high quality images. However, a flurry of recent papers points out that DMs are least private …

We show that the canonical approach for training differentially private GANs--updating the
discriminator with differentially private stochastic gradient descent (DPSGD)--can yield …

The availability of rich and vast data sources has greatly advanced machine learning
applications in various domains. However, data with privacy concerns comes with stringent …