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.
Smith, and Abhradeep Thakurta
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CR 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 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.