Synthetic Data: Methods, Use Cases, and Risks

• URL: http://arxiv.org/abs/2303.01230v3
• Date: Tue, 27 Feb 2024 07:03:15 GMT
• Title: Synthetic Data: Methods, Use Cases, and Risks
• Authors: Emiliano De Cristofaro
• Abstract summary: A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. 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.
• Score: 11.413309528464632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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