Privacy-preserving Prediction

COLT, 2018.

Cited by: 42|Bibtex|Views61
EI
Other Links: dblp.uni-trier.de|arxiv.org

Abstract:

Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learning without privac...More

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