Lower Bounds for Locally Private Estimation via Communication Complexity. Duchi, J. & Rogers, R. In 32nd Conference on Learning Theory, COLT 2019, volume 99, of Proceedings of Machine Learning Research, pages 1161–1191, 2019. PMLR. [DR19] Develops a communication-complexity-based methodology for lower bounds under LDP constraints in the blackboard communication model. Obtains lower bounds for mean estimation of Bernoulli products under general $\ell_p$ losses, as well as tight bounds for mean estimation of Gaussian and sparse Gaussian distributions under the $\ell_2$ loss.
bibtex @inproceedings{DR19,
title = {Lower Bounds for Locally Private Estimation via Communication Complexity},
author = {Duchi, John and Rogers, Ryan},
booktitle = {32nd Conference on Learning Theory, {COLT} 2019},
pages = {1161--1191},
year = {2019},
volume = {99},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
bibbase_note = {<div class="well well-small bibbase"><span class="bluecite">[DR19]</span> Develops a communication-complexity-based methodology for lower bounds under LDP constraints in the blackboard communication model. Obtains lower bounds for mean estimation of Bernoulli products under general $\ell_p$ losses, as well as tight bounds for mean estimation of Gaussian and sparse Gaussian distributions under the $\ell_2$ loss.}
}
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