The study addresses local private mean estimation of high-dimensional vectors, noting sub-optimal error or high complexity in existing solutions. A new framework, ProjUnit, is proposed, which offers computationally efficient algorithms with low communication complexity and near-optimal error by projecting inputs to a random low-dimensional subspace before normalization.
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