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 …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

The vast majority of artificial intelligence solutions are founded on game theory, and
differential privacy is emerging as perhaps the most rigorous and widely adopted privacy …

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 …

Abstract Generative Adversarial Networks (GANs) are one of the well-known models to
generate synthetic data including images, especially for research communities that cannot …

To improve real-world applications of machine learning, experienced modelers develop
intuition about their datasets, their models, and how the two interact. Manual inspection of …

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 …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

Limited data access is a longstanding barrier to data-driven research and development in
the networked systems community. In this work, we explore if and how generative …

In differential privacy (DP), a challenging problem is to generate synthetic datasets that
efficiently capture the useful information in the private data. The synthetic dataset enables …