# Differential Privacy in the Shuffle Model: A Survey of Separations

arXiv: https://arxiv.org/abs/2107.11839 · HTML: https://mlsanity.com/paper/2107.11839/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2021.

## Built on by (most cited first, 15 of 15)

- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Shuffle Private Linear Contextual Bandits](https://mlsanity.com/paper/2202.05567/) (2022)
- [On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence](https://mlsanity.com/paper/2309.02202/) (2023)
- [SoK: A Review of Differentially Private Linear Models For High-Dimensional Data](https://mlsanity.com/paper/2404.01141/) (2024)
- [Shuffle Gaussian Mechanism for Differential Privacy](https://mlsanity.com/paper/2206.09569/) (2022)
- [Concurrent Shuffle Differential Privacy Under Continual Observation](https://mlsanity.com/paper/2301.12535/) (2023)
- [Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning](https://mlsanity.com/paper/2412.07454/) (2024)
- [Privacy-Preserving Conformal Prediction Under Local Differential Privacy](https://mlsanity.com/paper/2505.15721/) (2025)
- [On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms](https://mlsanity.com/paper/2307.07744/) (2023)
- [DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.09902/) (2023)
- [How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations](https://mlsanity.com/paper/2507.01487/) (2025)
- [Uniformity Testing in the Shuffle Model: Simpler, Better, Faster](https://mlsanity.com/paper/2108.08987/) (2021)
- [Robust and differentially private stochastic linear bandits](https://mlsanity.com/paper/2304.11741/) (2023)
- [IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning](https://mlsanity.com/paper/2606.02563/) (2026)
- [Confidence Calibration of Deep Learning Systems](https://mlsanity.com/paper/2608.12100/) (2026)

## Abstract (arXiv)

Differential privacy is often studied in one of two models. In the central model, a single analyzer has the responsibility of performing a privacy-preserving computation on data. But in the local model, each data owner ensures their own privacy. Although it removes the need to trust the analyzer, local privacy comes at a price: a locally private protocol is less accurate than a centrally private counterpart when solving many learning and estimation problems. Protocols in the shuffle model are designed to attain the best of both worlds: recent work has shown high accuracy is possible with only a mild trust assumption. This survey paper gives an overview of novel shuffle protocols, along with lower bounds that establish the limits of the new model. We also summarize work that show the promise of interactivity in the shuffle model.