License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2022.7
URN: urn:nbn:de:0030-drops-165302
URL: https://drops.dagstuhl.de/opus/volltexte/2022/16530/
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Talwar, Kunal

Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation

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LIPIcs-FORC-2022-7.pdf (0.7 MB)


Abstract

Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications, these vectors are held by client devices, leading to a distributed summation problem. Standard Secure Multiparty Computation protocols for this problem are susceptible to poisoning attacks, where a client may have a large influence on the sum, without being detected.
In this work, we propose a poisoning-robust private summation protocol in the multiple-server setting, recently studied in PRIO [Henry Corrigan-Gibbs and Dan Boneh, 2017]. We present a protocol for vector summation that verifies that the Euclidean norm of each contribution is approximately bounded. We show that by relaxing the security constraint in SMC to a differential privacy like guarantee, one can improve over PRIO in terms of communication requirements as well as the client-side computation. Unlike SMC algorithms that inevitably cast integers to elements of a large finite field, our algorithms work over integers/reals, which may allow for additional efficiencies.

BibTeX - Entry

@InProceedings{talwar:LIPIcs.FORC.2022.7,
  author =	{Talwar, Kunal},
  title =	{{Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation}},
  booktitle =	{3rd Symposium on Foundations of Responsible Computing (FORC 2022)},
  pages =	{7:1--7:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-226-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{218},
  editor =	{Celis, L. Elisa},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16530},
  URN =		{urn:nbn:de:0030-drops-165302},
  doi =		{10.4230/LIPIcs.FORC.2022.7},
  annote =	{Keywords: Zero Knowledge, Secure Summation, Differential Privacy}
}

Keywords: Zero Knowledge, Secure Summation, Differential Privacy
Collection: 3rd Symposium on Foundations of Responsible Computing (FORC 2022)
Issue Date: 2022
Date of publication: 15.07.2022


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