Differentially Private Small Dataset Release Using Random Projections

Lovedeep Gondara, Ke Wang
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:639-648, 2020.

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 this Paper


BibTeX
@InProceedings{pmlr-v124-gondara20a, title = {Differentially Private Small Dataset Release Using Random Projections}, author = {Gondara, Lovedeep and Wang, Ke}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {639--648}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/gondara20a/gondara20a.pdf}, url = { http://proceedings.mlr.press/v124/gondara20a.html }, 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.} }
Endnote
%0 Conference Paper %T Differentially Private Small Dataset Release Using Random Projections %A Lovedeep Gondara %A Ke Wang %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-gondara20a %I PMLR %P 639--648 %U http://proceedings.mlr.press/v124/gondara20a.html %V 124 %X 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.
APA
Gondara, L. & Wang, K.. (2020). Differentially Private Small Dataset Release Using Random Projections. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:639-648 Available from http://proceedings.mlr.press/v124/gondara20a.html .

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