The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

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 …

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data
publishing that addresses the shortcomings of traditional anonymisation techniques. The …

Generative adversarial networks (GANs) implicitly learn the probability distribution of a
dataset and can draw samples from the distribution. This paper presents, Tabular GAN …

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied
across diverse domains. However, progress is impeded by the scarcity of training data due …

Decision forests are considered the best practice in many machine learning challenges,
mainly due to their superior predictive performance. However, simple models like decision …

Generative models trained with Differential Privacy (DP) can be used to generate synthetic
data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis …

Background: The exploitation of synthetic data in health care is at an early stage. Synthetic
data could unlock the potential within health care datasets that are too sensitive for release …

Synthetic datasets are gradually emerging as solutions for data sharing. Multiple synthetic
data generators have been introduced in the last decade fueled by advancement in machine …