2019

Differentially Private Summation with Multi-Message Shuffling

Balle, Borja, Bell, James, Gascon, Adria et al.

Understand

In recent work, Cheu et al.

  • (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{\epsilon, \delta}(1)$ error and $\Theta(\epsilon\sqrt{n})$ one-bit messages per party.
  • In contrast, every local model protocol for real summation must incur error $\Omega(1/\sqrt{n})$, and there exist protocols matching this lower bound which require just one bit of communication per party.
  • Whether this gap in number of messages is necessary was left open by Cheu et al.

Built on

  • How to recycle random bits

    Russell Impagliazzo and David Zuckerman · 1989

    Earlier work this paper cites.

  • Cryptography from anonymity

    Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky, and Amit Sahai · 2006

    Earlier work this paper cites.

  • Privacy-preserving aggregation of time-series data

    Elaine Shi, Richard Chow, T h. Hubert Chan, Dawn Song, and Eleanor Rieffel · 2011

    Earlier work this paper cites.

Similar

  • Privacy for free: Posterior sampling and stochastic gradient monte carlo

    Yu-Xiang Wang, Stephen E. Fienberg, and Alexander J. Smola · 2015

    Cited alongside, same era.

  • A comprehensive comparison of multiparty secure additions with differential privacy

    S. Goryczka and L. Xiong · 2017

    Cited alongside, same era.

  • The privacy blanket of the shuffle model

    Original

    Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019

    Cited alongside, same era.

Then

  • Distributed differential privacy via shuffling

    Albert Cheu, Adam D. Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019

    Closest in time.

  • Amplification by shuffling: From local to central differential privacy via anonymity

    Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019

    Closest in time.

  • Scalable and differentially private distributed aggregation in the shuffled model

    Original

    Badih Ghazi, Rasmus Pagh, and Ameya Velingker · 2019

    Closest in time.

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