On the Round Complexity of the Shuffle Model

09/28/2020
by   Amos Beimel, et al.
0

The shuffle model of differential privacy was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrusted analyzer that receives messages sent by participating parties via a shuffle functionality, the latter potentially disassociates messages from their senders. Prior work focused on one-round differentially private shuffle model protocols, demonstrating that functionalities such as addition and histograms can be performed in this model with accuracy levels similar to that of the curator model of differential privacy, where the computation is performed by a fully trusted party. Focusing on the round complexity of the shuffle model, we ask in this work what can be computed in the shuffle model of differential privacy with two rounds. Ishai et al. [FOCS 2006] showed how to use one round of the shuffle to establish secret keys between every two parties. Using this primitive to simulate a general secure multi-party protocol increases its round complexity by one. We show how two parties can use one round of the shuffle to send secret messages without having to first establish a secret key, hence retaining round complexity. Combining this primitive with the two-round semi-honest protocol of Applebaun et al. [TCC 2018], we obtain that every randomized functionality can be computed in the shuffle model with an honest majority, in merely two rounds. This includes any differentially private computation. We then move to examine differentially private computations in the shuffle model that (i) do not require the assumption of an honest majority, or (ii) do not admit one-round protocols, even with an honest majority. For that, we introduce two computational tasks: the common-element problem and the nested-common-element problem, for which we show separations between one-round and two-round protocols.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/20/2019

Differentially Private Summation with Multi-Message Shuffling

In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for n-p...
research
08/17/2022

Necessary Conditions in Multi-Server Differential Privacy

We consider protocols where users communicate with multiple servers to p...
research
05/03/2021

Channels of Small Log-Ratio Leakage and Characterization of Two-Party Differentially Private Computation

Consider a PPT two-party protocol π=(A,B) in which the parties get no pr...
research
02/16/2023

Practically Efficient Secure Computation of Rank-based Statistics Over Distributed Datasets

In this paper, we propose a practically efficient model for securely com...
research
12/18/2019

The power of synergy in differential privacy:Combining a small curator with local randomizers

Motivated by the desire to bridge the utility gap between local and trus...
research
04/14/2023

Separating Key Agreement and Computational Differential Privacy

Two party differential privacy allows two parties who do not trust each ...
research
09/27/2021

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message

The shuffle model of differential privacy has attracted attention in the...

Please sign up or login with your details

Forgot password? Click here to reset