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In this work we introduce a new protocol for vector aggregation in thecontext of the Shuffle Model, a recent model within Differential Privacy (DP).It sits between the Centralized Model, which prioritizes the level of accuracyover the secrecy of the data, and the Local Model, for which an improvement intrust is counteracted by a much higher noise requirement. The Shuffle Model wasdeveloped to provide a good balance between these two models through theaddition of a shuffling step, which unbinds the users from their data whilstmaintaining a moderate noise requirement. We provide a single message protocolfor the summation of real vectors in the Shuffle Model, using advancedcomposition results. Our contribution provides a mechanism to enable privateaggregation and analysis across more sophisticated structures such as matricesand higher-dimensional tensors, both of which are reliant on the functionalityof the vector case.

Applying the Shuffle Model of Differential Privacy to Vector Aggregation