@inproceedings{29340b4d5f694709b33603b32d9ae8cb,
title = "Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message",
abstract = "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 central differential privacy, while each user only sends 1 + o(1) short messages in expectation.",
keywords = "NOISE",
author = "Badih Ghazi and Ravi Kumar and Pasin Manurangsi and Rasmus Pagh and Amer Sinha",
year = "2021",
language = "English",
series = "Proceedings of Machine Learning Research",
publisher = "PMLR",
pages = "3692--3701",
editor = "M Meila and T Zhang",
booktitle = "Proceedings of the 38 th International Conference on Machine Learning",
note = "38th International Conference on Machine Learning (ICML) ; Conference date: 18-07-2021 Through 24-07-2021",
}