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- W4287777778 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." @default.
- W4287777778 created "2022-07-26" @default.
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- W4287777778 date "2020-05-16" @default.
- W4287777778 modified "2023-09-27" @default.
- W4287777778 title "Near Instance-Optimality in Differential Privacy" @default.
- W4287777778 doi "https://doi.org/10.48550/arxiv.2005.10630" @default.
- W4287777778 hasPublicationYear "2020" @default.
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