A random-projection differentially private kernel ERM method attains minimax-optimal excess risk bounds for squared and Lipschitz-smooth convex losses under local strong convexity, plus the first dimension-free bounds for objective-perturbation private linear ERM.
Duchi, Michael I
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Optimal differentially private kernel learning with random projection
A random-projection differentially private kernel ERM method attains minimax-optimal excess risk bounds for squared and Lipschitz-smooth convex losses under local strong convexity, plus the first dimension-free bounds for objective-perturbation private linear ERM.