Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy

Understand

Differential privacy (DP) data synthesizers support public release of sensitive information, offering theoretical guarantees for privacy but limited evidence of utility in practical settings.

• Utility is typically measured as the error on representative proxy tasks, such as descriptive statistics, accuracy of trained classifiers, or performance over a query workload.
• The ability for these results to generalize to practitioners' experience has been questioned in a number of settings, including the U.S.
• Census.