Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy. (arXiv:2208.12700v1 [cs.CR])

Differential privacy mechanisms are increasingly used to enable public
release of sensitive datasets, relying on strong theoretical guarantees for
privacy coupled with empirical evidence of utility. Utility is typically
measured as the error on representative proxy tasks, such as descriptive
statistics, multivariate correlations, or classification accuracy. In this
paper, we propose an alternative evaluation methodology for measuring the
utility of differentially private synthetic data in scientific research, a
measure we term "epistemic parity." Our methodology consists of reproducing
empirical conclusions …

differential privacy privacy