Differentially Private Small Dataset Release Using Random Projections
Abstract
Small datasets form a significant portion of releasable data in high sensitivity domains such as healthcare. But, providing differential privacy for small dataset release is a hard task, where current state-of-the-art methods suffer from severe utility loss. As a solution, we propose DPRP (Differentially Private Data Release via Random Projections), a reconstruction based approach for releasing differentially private small datasets. DPRP has several key advantages over the state-of-the-art. Using seven diverse real-life datasets, we show that DPRP outperforms the current state-of-the-art on a variety of tasks, under varying conditions, and for all privacy budgets.
Cite
Text
Gondara and Wang. "Differentially Private Small Dataset Release Using Random Projections." Uncertainty in Artificial Intelligence, 2020.Markdown
[Gondara and Wang. "Differentially Private Small Dataset Release Using Random Projections." Uncertainty in Artificial Intelligence, 2020.](https://mlanthology.org/uai/2020/gondara2020uai-differentially/)BibTeX
@inproceedings{gondara2020uai-differentially,
title = {{Differentially Private Small Dataset Release Using Random Projections}},
author = {Gondara, Lovedeep and Wang, Ke},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2020},
pages = {639-648},
volume = {124},
url = {https://mlanthology.org/uai/2020/gondara2020uai-differentially/}
}