Training even moderately-sized generative models with differentially-private stochastic
gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of …

Public data has been frequently used to improve the privacy-accuracy trade-off of
differentially private machine learning, but prior work largely assumes that this data come …

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 …

Although machine learning models trained on massive data have led to breakthroughs in
several areas, their deployment in privacy-sensitive domains remains limited due to …

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 …

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 …

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 …

Despite an increased demand for valuable data, the privacy concerns associated with
sensitive datasets present a barrier to data sharing. One may use differentially private …

While the success of deep learning relies on large amounts of training datasets, data is often
limited in privacy-sensitive domains. To address this challenge, generative model learning …