Geometry-Adaptive Mechanisms for Private Synthetic Data
A pure $\varepsilon$-differentially private mechanism that combines private depth selection with the authors' pruned variant of the Private Measure Mechanism (PMM) is proposed, and a lower bound under a corresponding internal packing-growth condition is proved, showing that the exponent $1/k$ is sharp within this frame...