Multidimensional simplex transformations on [0,1]-bounded variables extend the free lunch for private dataset size estimation, refining sufficient statistics for differentially private simple linear regression via OLS with claimed analytical and numerical gains.
Since the noise scale isb= ∆ 1(f)/ε1 = 1/ε1, the Laplace mechanism theorem guaranteesε 1-differential privacy
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Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
Multidimensional simplex transformations on [0,1]-bounded variables extend the free lunch for private dataset size estimation, refining sufficient statistics for differentially private simple linear regression via OLS with claimed analytical and numerical gains.