Synthetic Data: Methods, Use Cases, and Risks

TL;DR

Synthetic data offers a promising way to share useful datasets while protecting privacy, but it faces challenges and limitations that need careful consideration.

Contribution

This paper provides an introductory overview of synthetic data, discussing its applications, privacy concerns, and inherent limitations as a privacy-preserving technology.

Findings

Synthetic data can enable data sharing without exposing sensitive information.

There are significant privacy challenges and unaddressed risks associated with synthetic data.

Synthetic data has limitations that restrict its effectiveness as a privacy-enhancing solution.

Abstract

Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data -- more precisely, having similar statistical properties. In this article, we provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.