Synthetic Data: Methods, Use Cases, and Risks
This is an incremental review article that addresses privacy concerns for researchers and organizations handling sensitive data.
The paper tackles the problem of sharing sensitive datasets by proposing synthetic data as an alternative, discussing its use cases, privacy challenges, and limitations as a privacy-enhancing technology.
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.