We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradients in private federated learning, where the aggregate itself is protected via noise addition to ensure differential privacy. Existing approaches require communication scaling with the dimensionality, and thus limit the dimensionality of vectors one can efficiently process in this setup.
We propose PREAMBLE: {bf Pr}ivate {bf E}fficient {bf A}ggregation {bf M}echanism via {bf BL}ock-sparse {bf E}uclidean Vectors…
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