Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

To prevent unintentional data leakage, research community has resorted to data generators
that can produce differentially private data for model training. However, for the sake of the …

The popularity of deep learning has led to the curation of a vast number of massive and
multifarious datasets. Despite having close-to-human performance on individual tasks …

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 …

Sharing data with third parties is essential for advancing science, but it is becoming more
and more difficult with the rise of data protection regulations, ethical restrictions, and growing …

This work presents a systematic benchmark of differentially private synthetic data generation
algorithms that can generate tabular data. Utility of the synthetic data is evaluated by …

The wide-spread availability of rich data has fueled the growth of machine learning
applications in numerous domains. However, growth in domains with highly-sensitive data …

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

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 …

Differentially private data generation techniques have become a promising solution to the
data privacy challenge––it enables sharing of data while complying with rigorous privacy …