# How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations

arXiv: https://arxiv.org/abs/2507.01487 · HTML: https://mlsanity.com/paper/2507.01487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 151 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 30 of 58)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion](https://mlsanity.com/paper/2007.02220/) (2020)
- [Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning](https://mlsanity.com/paper/2108.10241/) (2021)
- [Distributed Differential Privacy via Shuffling](https://mlsanity.com/paper/1808.01394/) (2018)
- [Privacy Amplification by Iteration](https://mlsanity.com/paper/1808.06651/) (2018)
- [Byzantine Stochastic Gradient Descent](https://mlsanity.com/paper/1803.08917/) (2018)
- [The Privacy Blanket of the Shuffle Model](https://mlsanity.com/paper/1903.02837/) (2019)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [Poisoning the Unlabeled Dataset of Semi-Supervised Learning](https://mlsanity.com/paper/2105.01622/) (2021)
- [Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation](https://mlsanity.com/paper/2001.03618/) (2020)
- [Privacy Amplification via Random Check-Ins](https://mlsanity.com/paper/2007.06605/) (2020)
- [Private Summation in the Multi-Message Shuffle Model](https://mlsanity.com/paper/2002.00817/) (2020)
- [Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacy](https://mlsanity.com/paper/2208.04591/) (2022)
- [Privacy Amplification by Decentralization](https://mlsanity.com/paper/2012.05326/) (2020)
- [EIFFeL: Ensuring Integrity for Federated Learning](https://mlsanity.com/paper/2112.12727/) (2021)
- [FLAME: Differentially Private Federated Learning in the Shuffle Model](https://mlsanity.com/paper/2009.08063/) (2020)
- [FederBoost: Private Federated Learning for GBDT](https://mlsanity.com/paper/2011.02796/) (2020)
- [Data Poisoning Attacks to Local Differential Privacy Protocols](https://mlsanity.com/paper/1911.02046/) (2019)
- [Scalable and Differentially Private Distributed Aggregation in the Shuffled Model](https://mlsanity.com/paper/1906.08320/) (2019)
- [An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging](https://mlsanity.com/paper/2006.07218/) (2020)
- [Separating Local & Shuffled Differential Privacy via Histograms](https://mlsanity.com/paper/1911.06879/) (2019)
- [Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead](https://mlsanity.com/paper/2106.04247/) (2021)
- [Software Grand Exposure: SGX Cache Attacks Are Practical](https://mlsanity.com/paper/1702.07521/) (2017)
- [Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX](https://mlsanity.com/paper/1705.07289/) (2017)

## Built on by (most cited first, 1 of 1)

- [Prediction with Expert Advice under Local Differential Privacy](https://mlsanity.com/paper/2512.06971/) (2025)

## Abstract (arXiv)

Ishai et al. (FOCS'06) introduced secure shuffling as an efficient building block for private data aggregation. Recently, the field of differential privacy has revived interest in secure shufflers by highlighting the privacy amplification they can provide in various computations. Although several works argue for the utility of secure shufflers, they often treat them as black boxes; overlooking the practical vulnerabilities and performance trade-offs of existing implementations. This leaves a central question open: what makes a good secure shuffler? This survey addresses that question by identifying, categorizing, and comparing 26 secure protocols that realize the necessary shuffling functionality. To enable a meaningful comparison, we adapt and unify existing security definitions into a consistent set of properties. We also present an overview of privacy-preserving technologies that rely on secure shufflers, offer practical guidelines for selecting appropriate protocols, and outline promising directions for future work.