License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITC.2020.14
URN: urn:nbn:de:0030-drops-121195
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/12119/
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Beimel, Amos ; Korolova, Aleksandra ; Nissim, Kobbi ; Sheffet, Or ; Stemmer, Uri

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

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Abstract

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.

BibTeX - Entry

@InProceedings{beimel_et_al:LIPIcs:2020:12119,
  author =	{Amos Beimel and Aleksandra Korolova and Kobbi Nissim and Or Sheffet and Uri Stemmer},
  title =	{{The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers}},
  booktitle =	{1st Conference on Information-Theoretic Cryptography (ITC 2020)},
  pages =	{14:1--14:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-151-1},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{163},
  editor =	{Yael Tauman Kalai and Adam D. Smith and Daniel Wichs},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12119},
  URN =		{urn:nbn:de:0030-drops-121195},
  doi =		{10.4230/LIPIcs.ITC.2020.14},
  annote =	{Keywords: differential privacy, hybrid model, private learning, local model}
}

Keywords: differential privacy, hybrid model, private learning, local model
Collection: 1st Conference on Information-Theoretic Cryptography (ITC 2020)
Issue Date: 2020
Date of publication: 04.06.2020

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