  Many ground-breaking advancements in machine learning can be attributed to
the availability of a large volume of rich data. Unfortunately, many
large-scale datasets are highly sensitive, such as healthcare data, and are not
widely available to the machine learning community. Generating synthetic data
with privacy guarantees provides one such solution, allowing meaningful
research to be carried out "at scale" - by allowing the entirety of the machine
learning community to potentially accelerate progress within a given field. In
this article, we provide a high-level view of synthetic data: what it means,
how we might evaluate it and how we might use it.
