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arXiv · 2005.10630

Near Instance-Optimality in Differential Privacy

Hilal Asi·John C. Duchi
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Abstract

We develop two notions of instance optimality in differential privacy, inspired by classical statistical theory: one by defining a local minimax risk and the other by considering unbiased mechanisms and analogizing the Cramer-Rao bound, and we show that the local modulus of continuity of the estimand of interest completely determines these quantities. We also develop a complementary collection mechanisms, which we term the inverse sensitivity mechanisms, which are instance optimal (or nearly instance optimal) for a large class of estimands. Moreover, these mechanisms uniformly outperform the smooth sensitivity framework on each instance for several function classes of interest, including real-valued continuous functions. We carefully present two instantiations of the mechanisms for median and robust regression estimation with corresponding experiments.

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Hilal Asi, John C. Duchi. 2020-05-16. Near Instance-Optimality in Differential Privacy. https://arxiv.org/abs/2005.10630

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