NeurIPS 2018poster34 citations
The Price of Privacy for Low-rank Factorization
Abstract
In this paper, we study what price one has to pay to release \emph{differentially private low-rank factorization} of a matrix. We consider various settings that are close to the real world applications of low-rank factorization: (i) the manner in which matrices are updated (row by row or in an arbitrary manner), (ii) whether matrices are distributed or not, and (iii) how the output is produced (once at the end of all updates, also known as \emph{one-shot algorithms} or continually). Even though these settings are well studied without privacy, surprisingly, there are no private algorithm for these settings (except when a matrix is updated row by row). We present the first set of differentially private algorithms for all these settings.
BibTeX
@inproceedings{NEURIPS2018_2eace51d,
author = {Upadhyay, Jalaj},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {The Price of Privacy for Low-rank Factorization},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/2eace51d8f796d04991c831a07059758-Paper.pdf},
volume = {31},
year = {2018}
}