Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data
This work addresses the need for systematic evaluation of synthetic data generators to ensure privacy-respecting data sharing, though it is incremental as it builds on existing assessment methods.
The authors tackled the challenge of evaluating synthetic data quality by introducing a holdout-based framework to assess fidelity and privacy for mixed-type tabular data, demonstrating it across seven synthetic data solutions and four datasets, and comparing it to traditional statistical disclosure techniques.
AI-based data synthesis has seen rapid progress over the last several years, and is increasingly recognized for its promise to enable privacy-respecting high-fidelity data sharing. However, adequately evaluating the quality of generated synthetic datasets is still an open challenge. We introduce and demonstrate a holdout-based empirical assessment framework for quantifying the fidelity as well as the privacy risk of synthetic data solutions for mixed-type tabular data. Measuring fidelity is based on statistical distances of lower-dimensional marginal distributions, which provide a model-free and easy-to-communicate empirical metric for the representativeness of a synthetic dataset. Privacy risk is assessed by calculating the individual-level distances to closest record with respect to the training data. By showing that the synthetic samples are just as close to the training as to the holdout data, we yield strong evidence that the synthesizer indeed learned to generalize patterns and is independent of individual training records. We demonstrate the presented framework for seven distinct synthetic data solutions across four mixed-type datasets and compare these to more traditional statistical disclosure techniques. The results highlight the need to systematically assess the fidelity just as well as the privacy of these emerging class of synthetic data generators.