AI RESEARCH
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
arXiv CS.LG
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ArXi:2510.16974v3 Announce Type: replace In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (DP) linear regression, but mostly on point estimation with limited attention to uncertainty quantification. Meanwhile, synthetic data generation (SDG) is increasingly important for reproducibility studies, yet current DP linear regression methods do not readily it.