2019

Improved Summation from Shuffling

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

Understand

A protocol by Ishai et al.\ (FOCS 2006) showing how to implement distributed $n$-party summation from secure shuffling has regained relevance in the context of the recently proposed \emph{shuffle model} of differential privacy, as it allows to attain the accuracy levels of the curator model at a moderate communication cost.

  • To achieve statistical security $2^{-\sigma}$, the protocol by Ishai et al.\ requires the number of messages sent by each party to {\em grow} logarithmically with $n$ as $O(\log n + \sigma)$.
  • In this note we give an improved analysis achieving a dependency of the form $O(1+\sigma/\log n)$.
  • Conceptually, this addresses the intuitive question left open by Ishai et al.\ of whether the shuffling step in their protocol provides a "hiding in the crowd" amplification effect as $n$ increases.

Built on

  • Cryptography from anonymity

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

    Earlier work this paper cites.

  • Prochlo: Strong privacy for analytics in the crowd

    Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnés, and Bernhard Seefeld · 2017

    Earlier work this paper cites.

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