Differentially Private Summation with Multi-Message Shuffling
Not on the map. Why?
Abstract (arXiv)
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for n-party real summation in the shuffle model of differential privacy with Oε, δ(1) error and Theta(ε√n) one-bit messages per party. In contrast, every local model protocol for real summation must incur error Omega(1/√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. In this note we show a protocol with O(1/ε) error and O(log(n/δ)) messages of size O(log(n)) per party. This protocol is based on the work of Ishai et al. (FOCS 2006) showing how to implement distributed summation from secure shuffling, and the observation that this allows simulating the Laplace mechanism in the shuffle model.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 5; 2022: 3; 2023: 0; 2024: 0; 2025: 1.
9 papers.
- 2022Shuffle Private Linear Contextual Banditscited by 27
- 2022Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averagingcited by 27
- 2021Differentially Private Multi-Armed Bandits in the Shuffle Modelcited by 24
- 2021Shuffle Private Stochastic Convex Optimizationcited by 17
- 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
- 2021Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learningcited by 6
- 2022FedPerm: Private and Robust Federated Learning by Parameter Permutationcited by 3
- 2021The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Modelcited by 1
- 2025Fully Decentralized Certified Unlearningcited by 1
- 2025Fully Decentralized Certified Unlearning9 Dec 2025
- 2022FedPerm: Private and Robust Federated Learning by Parameter Permutation16 Aug 2022
- 2022Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging10 Jun 2022
- 2022Shuffle Private Linear Contextual Bandits11 Feb 2022
- 2021Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning19 Jul 2021
- 2021Shuffle Private Stochastic Convex Optimization17 Jun 2021
- 2021Differentially Private Multi-Armed Bandits in the Shuffle Model5 Jun 2021
- 2021On the Renyi Differential Privacy of the Shuffle Model11 May 2021
- 2021The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model29 Mar 2021