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The power of synergy in differential privacy: Combining a small curator with local randomizers.

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Predicting Consumer Behavior in Commerce Search., , и . ICML, icml.cc / Omnipress, (2012)A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem., и . NeurIPS, (2022)Improved Spectral-Norm Bounds for Clustering., и . APPROX-RANDOM, том 7408 из Lecture Notes in Computer Science, стр. 37-49. Springer, (2012)The power of synergy in differential privacy: Combining a small curator with local randomizers., , , , и . CoRR, (2019)Differentially Private Algorithms for Learning Mixtures of Separated Gaussians., , , и . NeurIPS, стр. 168-180. (2019)Differentially Private Contextual Linear Bandits., и . NeurIPS, стр. 4301-4311. (2018)The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers., , , , и . ITC, том 163 из LIPIcs, стр. 14:1-14:25. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2020)Differentially Private Approximations of a Convex Hull in Low Dimensions., и . ITC, том 199 из LIPIcs, стр. 18:1-18:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2021)Differentially private data analysis of social networks via restricted sensitivity., , , и . ITCS, стр. 87-96. ACM, (2013)Locally Private Hypothesis Testing.. ICML, том 80 из Proceedings of Machine Learning Research, стр. 4612-4621. PMLR, (2018)