Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
As data-privacy regulations tighten and statistical models are increasingly deployed on sensitive human-sourced data, privacy-preserving linear regression has become a critical necessity. For the add-remove DP model, Kulesza et al. (2024) and Fitzsimons et al. (2024) have independently shown that the size of the dataset -- an important statistic for linear regression -- can be privately estimated for "free", via a simplex transformation of bounded variables and private sum queries on the transformed variables. In this work, we extend this free lunch result via carefully crafted multidimensional simplex transformations to variables and functions that are bounded in the interval [0,1]. We s...
| Authors | Sasmita Harini S, Anshoo Tandon |
|---|---|
| Primary category | cs.IT |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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