文章基本信息

标题：The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers
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作者：Amos Beimel ; Aleksandra Korolova ; Kobbi Nissim 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2020
卷号：163
页码：14:1-14:25
DOI：10.4230/LIPIcs.ITC.2020.14
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al., USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users. We focus on the regime where m âª n and study the new capabilities of this (m,n)-hybrid model. We show that, despite the fact that the hybrid model adds no significant new capabilities for the basic task of simple hypothesis-testing, there are many other tasks (under a wide range of parameters) that can be solved in the hybrid model yet cannot be solved either by the curator or by the local-users separately. Moreover, we exhibit additional tasks where at least one round of interaction between the curator and the local-users is necessary - namely, no hybrid model protocol without such interaction can solve these tasks. Taken together, our results show that the combination of the local model with a small curator can become part of a promising toolkit for designing and implementing differential privacy.
关键词：differential privacy; hybrid model; private learning; local model