Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families
Many differentially private (DP) data release systems either output DP synthetic data and leave analysts to perform inference as usual, which can lead to severe miscalibration, or output a DP point estimate without a principled way to do uncertainty quantification. This paper develops a clean and tractable middle ground for exponential families: release only DP sufficient statistics, then perform noise-calibrated likelihood-based inference and optional parametric synthetic data generation as post-processing. Our contributions are: (1) a general recipe for approximate-DP release of clipped sufficient statistics under the Gaussian mechanism; (2) asymptotic normality, explicit variance infla...
| Authors | Amir Asiaee, Samhita Pal |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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