ICML 2021spotlight44 citations
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha
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.
BibTeX
@InProceedings{pmlr-v139-ghazi21a,
title = {Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message},
author = {Ghazi, Badih and Kumar, Ravi and Manurangsi, Pasin and Pagh, Rasmus and Sinha, Amer},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {3692--3701},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/ghazi21a/ghazi21a.pdf},
url = {https://proceedings.mlr.press/v139/ghazi21a.html},
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.}
}