A distributed model where a public linear sketch is computed securely over client-side noise achieves central-model-like utility for private low-rank approximation and ridge regression, with error independent of the number of clients.
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Distributed Differentially Private Data Analytics via Secure Sketching
A distributed model where a public linear sketch is computed securely over client-side noise achieves central-model-like utility for private low-rank approximation and ridge regression, with error independent of the number of clients.