Private Summation in the Multi-Message Shuffle Model

Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

The shuffle model of differential privacy (Erlingsson et al. SODA 2019; Cheu et al. EUROCRYPT 2019) and its close relative encode-shuffle-analyze (Bittau et al. SOSP 2017) provide a fertile middle ground between the well-known local and central models. Similarly to the local model, the shuffle model assumes an untrusted data collector who receives privatized messages from users, but in this case a secure shuffler is used to transmit messages from users to the collector in a way that hides which messages came from which user. An interesting feature of the shuffle model is that increasing the amount of messages sent by each user can lead to protocols with accuracies comparable to the ones achievable in the central model. In particular, for the problem of privately computing the sum of n bounded real values held by n different users, Cheu et al. showed that O(sqrtn ) messages per user suffice to achieve O(1) error (the optimal rate in the central model), while Balle et al. (CRYPTO 2019) recently showed that a single message per user leads to Theta(n^1/3 ) MSE (mean squared error), a rate strictly in-between what is achievable in the local and central models. This paper introduces two new protocols for summation in the shuffle model with improved accuracy and communication trade-offs. Our first contribution is a recursive construction based on the protocol from Balle et al. mentioned above, providing poly(log log n) error with O(log log n) messages per user. The second contribution is a protocol with O(1) error and O(1) messages per user based on a novel analysis of the reduction from secure summation to shuffling introduced by Ishai et al. (FOCS 2006) (the original reduction required O(log n) messages per user). We also provide a numerical evaluation showing that our protocols provide good trade-offs between privacy, accuracy and communication for realistic values of n.

Original languageEnglish
Title of host publicationCCS 2020 - Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages657-676
Number of pages20
ISBN (Electronic)9781450370899
DOIs
StatePublished - 30 Oct 2020
Externally publishedYes
Event27th ACM SIGSAC Conference on Computer and Communications Security, CCS 2020 - Virtual, Online, United States
Duration: 9 Nov 202013 Nov 2020

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference27th ACM SIGSAC Conference on Computer and Communications Security, CCS 2020
Country/TerritoryUnited States
CityVirtual, Online
Period9/11/2013/11/20

Keywords

  • differential privacy
  • real summation
  • secure summation
  • shuffle model

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