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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


Keywords: [ Social Aspects of Machine Learning ] [ Privacy, Anonymity, and Security ]


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.

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