  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.
