The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work, we study the problem of summing (aggregating) real numbers or integers, a basic primitive in numerous machine learning tasks, in the shuffle model. We give a protocol achieving error arbitrarily close to that of the (Discrete) Laplace mechanism in the central model, while each user only sends 1+o(1) short messages in expectation.
@article{arxiv.2109.13158,
title = {Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message},
author = {Badih Ghazi and Ravi Kumar and Pasin Manurangsi and Rasmus Pagh and Amer Sinha},
journal= {arXiv preprint arXiv:2109.13158},
year = {2021}
}