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 …

We propose, implement, and evaluate a new algo-rithm for releasing answers to very large
numbersof statistical queries likek-way marginals, sub-ject to differential privacy. Our …

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 …

We study the problem of efficiently generating differentially private synthetic data that
approximate the statistical properties of an underlying sensitive dataset. In recent years …

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 …

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 …

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 …

Generative models trained with Differential Privacy (DP) are increasingly used to produce
synthetic data while reducing privacy risks. Navigating their specific privacy-utility tradeoffs …

Generative models trained with Differential Privacy (DP) can produce synthetic data while
reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding the …