# Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient

arXiv: https://arxiv.org/abs/2305.17634 · HTML: https://mlsanity.com/paper/2305.17634/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2023.

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

- [Learning from End User Data with Shuffled Differential Privacy over Kernel Densities](https://mlsanity.com/paper/2502.14087/) (2025)

## Abstract (arXiv)

We obtain a new protocol for binary counting in the varepsilon-shuffle-DP model with error O(1/varepsilon) and expected communication tildeOleft(fraclog nvarepsilonright) messages per user. Previous protocols incur either an error of O(1/varepsilon1.5) with O_varepsilon(logn) messages per user (Ghazi et al., ITC 2020) or an error of O(1/varepsilon) with O_varepsilon(n2.5) messages per user (Cheu and Yan, TPDP 2022). Using the new protocol, we obtained improved varepsilon-shuffle-DP protocols for real summation and histograms.