  In this work we introduce a new protocol for vector aggregation in the
context of the Shuffle Model, a recent model within Differential Privacy (DP).
It sits between the Centralized Model, which prioritizes the level of accuracy
over the secrecy of the data, and the Local Model, for which an improvement in
trust is counteracted by a much higher noise requirement. The Shuffle Model was
developed to provide a good balance between these two models through the
addition of a shuffling step, which unbinds the users from their data whilst
maintaining a moderate noise requirement. We provide a single message protocol
for the summation of real vectors in the Shuffle Model, using advanced
composition results. Our contribution provides a mechanism to enable private
aggregation and analysis across more sophisticated structures such as matrices
and higher-dimensional tensors, both of which are reliant on the functionality
of the vector case.
