A three-method auditing framework detects with roughly 87 to 97 percent accuracy whether classifiers, generators, and t-SNE plots were trained on or derived from LLM-generated synthetic data.
Synthetic Data: Methods, Use Cases, and Risks
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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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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
A three-method auditing framework detects with roughly 87 to 97 percent accuracy whether classifiers, generators, and t-SNE plots were trained on or derived from LLM-generated synthetic data.