Synthetic Data: Methods, Use Cases, and Risks

arXiv:2303.01230v3 Announce Type: replace
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, …

analytics applications arxiv can cases community cs.ai cs.cr cs.cy data datasets enable industry information momentum nature organizations privacy research risks sensitive sharing synthetic synthetic data use cases