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

TL;DR

This paper discusses the potential of synthetic data to democratize access to large, sensitive datasets, enabling faster and more targeted development of machine learning methods while addressing privacy concerns.

Contribution

It provides a high-level overview of synthetic data, including its definition, evaluation methods, and practical applications in machine learning research.

Findings

Synthetic data can enable broader access to sensitive datasets.

Evaluation metrics for synthetic data are crucial for assessing quality.

Synthetic data has the potential to accelerate machine learning progress.

Abstract

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.