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.
Title resolution pending
1 Pith paper cite this work, alongside 85 external citations. Polarity classification is still indexing.
1
Pith paper citing it
85
external citations · OpenAlex
fields
cs.CR 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.