Differentially Private M-Estimators
Abstract
This paper studies privacy preserving M-estimators using perturbed histograms. The proposed approach allows the release of a wide class of M-estimators with both differential privacy and statistical utility without knowing a priori the particular inference procedure. The performance of the proposed method is demonstrated through a careful study of the convergence rates. A practical algorithm is given and applied on a real world data set containing both continuous and categorical variables.
Cite
Text
Lei. "Differentially Private M-Estimators." Neural Information Processing Systems, 2011.Markdown
[Lei. "Differentially Private M-Estimators." Neural Information Processing Systems, 2011.](https://mlanthology.org/neurips/2011/lei2011neurips-differentially/)BibTeX
@inproceedings{lei2011neurips-differentially,
title = {{Differentially Private M-Estimators}},
author = {Lei, Jing},
booktitle = {Neural Information Processing Systems},
year = {2011},
pages = {361-369},
url = {https://mlanthology.org/neurips/2011/lei2011neurips-differentially/}
}