Local Private Hypothesis Testing: Chi-Square Tests
Abstract
The local model for differential privacy is emerging as the reference model for practical applications of collecting and sharing sensitive information while satisfying strong privacy guarantees. In the local model, there is no trusted entity which is allowed to have each individual’s raw data as is assumed in the traditional curator model. Individuals’ data are usually perturbed before sharing them. We explore the design of private hypothesis tests in the local model, where each data entry is perturbed to ensure the privacy of each participant. Specifically, we analyze locally private chi-square tests for goodness of fit and independence testing.
Cite
Text
Gaboardi and Rogers. "Local Private Hypothesis Testing: Chi-Square Tests." International Conference on Machine Learning, 2018.Markdown
[Gaboardi and Rogers. "Local Private Hypothesis Testing: Chi-Square Tests." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/gaboardi2018icml-local/)BibTeX
@inproceedings{gaboardi2018icml-local,
title = {{Local Private Hypothesis Testing: Chi-Square Tests}},
author = {Gaboardi, Marco and Rogers, Ryan},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {1626-1635},
volume = {80},
url = {https://mlanthology.org/icml/2018/gaboardi2018icml-local/}
}