Differentially Private Chi-Squared Test by Unit Circle Mechanism

Abstract

This paper develops differentially private mechanisms for $\chi^2$ test of independence. While existing works put their effort into properly controlling the type-I error, in addition to that, we investigate the type-II error of differentially private mechanisms. Based on the analysis, we present unit circle mechanism: a novel differentially private mechanism based on the geometrical property of the test statistics. Compared to existing output perturbation mechanisms, our mechanism improves the dominated term of the type-II error from $O(1)$ to $O(\exp(-\sqrt{N}))$ where $N$ is the sample size. Furthermore, we introduce novel procedures for multiple $\chi^2$ tests by incorporating the unit circle mechanism into the sparse vector technique and the exponential mechanism. These procedures can control the family-wise error rate (FWER) properly, which has never been attained by existing mechanisms.

Cite

Text

Kakizaki et al. "Differentially Private Chi-Squared Test by Unit Circle Mechanism." International Conference on Machine Learning, 2017.

Markdown

[Kakizaki et al. "Differentially Private Chi-Squared Test by Unit Circle Mechanism." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/kakizaki2017icml-differentially/)

BibTeX

@inproceedings{kakizaki2017icml-differentially,
  title     = {{Differentially Private Chi-Squared Test by Unit Circle Mechanism}},
  author    = {Kakizaki, Kazuya and Fukuchi, Kazuto and Sakuma, Jun},
  booktitle = {International Conference on Machine Learning},
  year      = {2017},
  pages     = {1761-1770},
  volume    = {70},
  url       = {https://mlanthology.org/icml/2017/kakizaki2017icml-differentially/}
}