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This content will become publicly available on May 14, 2025

Title: Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
Differential privacy (DP) data synthesizers are increasingly proposed to afford public release of sensitive information, offering theoretical guarantees for privacy (and, in some cases, utility), but limited empirical evidence of utility in practical settings. Utility is typically measured as the error on representative proxy tasks, such as descriptive statistics, multivariate correlations, the 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. In this paper, we propose an evaluation methodology for synthetic data that avoids assumptions about the representativeness of proxy tasks, instead measuring the likelihood that published conclusions would change had the authors used synthetic data, a condition we call epistemic parity. Our methodology consists of reproducing empirical conclusions of peer-reviewed papers on real, publicly available data, then re-running these experiments a second time on DP synthetic data and comparing the results.  more » « less
Award ID(s):
1916505 1922658
PAR ID:
10514473
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
ACM SIGMOD Record
Volume:
53
Issue:
1
ISSN:
0163-5808
Page Range / eLocation ID:
65 to 74
Subject(s) / Keyword(s):
responsible AI differential privacy data synthesis benchmarking
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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