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Many ground-breaking advancements in machine learning can be attributed tothe availability of a large volume of rich data. Unfortunately, manylarge-scale datasets are highly sensitive, such as healthcare data, and are notwidely available to the machine learning community. Generating synthetic datawith privacy guarantees provides one such solution, allowing meaningfulresearch to be carried out "at scale" - by allowing the entirety of the machinelearning community to potentially accelerate progress within a given field. Inthis article, we provide a high-level view of synthetic data: what it means,how we might evaluate it and how we might use it.

Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods