Synthetic Data Privacy Metrics
This work addresses the lack of standardization in privacy metrics for synthetic data, which is crucial for researchers and practitioners handling sensitive datasets.
The paper reviews existing privacy metrics for synthetic data, analyzing their pros and cons, and examines best practices like differential privacy to enhance privacy in generative models.
Recent advancements in generative AI have made it possible to create synthetic datasets that can be as accurate as real-world data for training AI models, powering statistical insights, and fostering collaboration with sensitive datasets while offering strong privacy guarantees. Effectively measuring the empirical privacy of synthetic data is an important step in the process. However, while there is a multitude of new privacy metrics being published every day, there currently is no standardization. In this paper, we review the pros and cons of popular metrics that include simulations of adversarial attacks. We also review current best practices for amending generative models to enhance the privacy of the data they create (e.g. differential privacy).