AI RESEARCH
Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
arXiv CS.LG
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ArXi:2604.11820v1 Announce Type: cross 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 and Fitzsimons 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.