On differentially private low rank approximation

DOI10.1137/1.9781611973105.101zbMath1423.68595OpenAlexW4239784985MaRDI QIDQ5741810

Michael Kapralov, Kunal Talwar

Publication date: 15 May 2019

Published in: Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/b71e4af30a998e1ee94535c3664e277d3662178a

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Learning and adaptive systems in artificial intelligence (68T05) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17) Approximation algorithms (68W25)

Related Items (4)

Covariance's loss is privacy's gain: computationally efficient, private and accurate synthetic data ⋮ Differentially-private learning of low dimensional manifolds ⋮ Principal component analysis in the local differential privacy model ⋮ Frequent Directions: Simple and Deterministic Matrix Sketching

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