Covariance's loss is privacy's gain: computationally efficient, private and accurate synthetic data

DOI10.1007/s10208-022-09591-7arXiv2107.05824OpenAlexW3181087951MaRDI QIDQ6118081

Thomas Strohmer, March Boedihardjo, R. V. Vershinin

Publication date: 23 February 2024

Published in: Foundations of Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.05824

zbMATH Keywords

differential privacysynthetic datacovariance loss

Mathematics Subject Classification ID

Privacy of data (68P27)

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