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The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers

Authors Amos Beimel , Aleksandra Korolova, Kobbi Nissim , Or Sheffet , Uri Stemmer

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Amos Beimel
  • Dept. of Computer Science, Ben-Gurion University, Beer-Sheva, Israel
Aleksandra Korolova
  • Dept. of Computer Science, University of Southern California, Los Angeles, CA, USA
Kobbi Nissim
  • Dept. of Computer Science, Georgetown University, Washington, DC, USA
Or Sheffet
  • Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
Uri Stemmer
  • Dept. of Computer Science, Ben-Gurion University, Beer-Sheva, Israel
  • Google Research


We thank Adam Smith for suggesting the select-then-estimate task discussed in the introduction.

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Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, and Uri Stemmer. The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. In 1st Conference on Information-Theoretic Cryptography (ITC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 163, pp. 14:1-14:25, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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 ≪ 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.

Subject Classification

ACM Subject Classification
  • Security and privacy → Privacy-preserving protocols
  • differential privacy
  • hybrid model
  • private learning
  • local model


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