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.
@article{arxiv.2303.01230,
title = {Synthetic Data: Methods, Use Cases, and Risks},
author = {Emiliano De Cristofaro},
journal= {arXiv preprint arXiv:2303.01230},
year = {2024}
}