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The authors' lower bounds match the Binary Tree Mechanism instantiated with Laplace noise, establishing its asymptotic optimality under both pure differential privacy and approximate differential privacy in the standard regime of $\delta \ll1/n$.
Trust in AI-driven decisions can be systematically documented, ensuring accountability at the crucial output-to-action boundary in bioscience research.
R茅nyi differential privacy unlocks tighter privacy guarantees in partition selection, but releasing partition frequencies comes at a cost.
