%0 Journal Article %1 kifer2016class %A Kifer, Daniel %A Rogers, Ryan %D 2016 %K differential-privacy stats %T A New Class of Private Chi-Square Tests %U http://arxiv.org/abs/1610.07662 %X In this paper, we develop new test statistics for private hypothesis testing. These statistics are designed specifically so that their asymptotic distributions, after accounting for noise added for privacy concerns, match the asymptotics of the classical (non-private) chi-square tests for testing if the multinomial data parameters lie in lower dimensional manifolds (examples include goodness of fit and independence testing). Empirically, these new test statistics outperform prior work, which focused on noisy versions of existing statistics.

@article{kifer2016class, abstract = {In this paper, we develop new test statistics for private hypothesis testing. These statistics are designed specifically so that their asymptotic distributions, after accounting for noise added for privacy concerns, match the asymptotics of the classical (non-private) chi-square tests for testing if the multinomial data parameters lie in lower dimensional manifolds (examples include goodness of fit and independence testing). Empirically, these new test statistics outperform prior work, which focused on noisy versions of existing statistics.}, added-at = {2019-09-19T13:35:56.000+0200}, author = {Kifer, Daniel and Rogers, Ryan}, biburl = {https://www.bibsonomy.org/bibtex/2ab8c74bca4e1dfadffd4eda34f196e93/kirk86}, description = {[1610.07662] A New Class of Private Chi-Square Tests}, interhash = {13e69ee22bda84da301b655aa3a9cd20}, intrahash = {ab8c74bca4e1dfadffd4eda34f196e93}, keywords = {differential-privacy stats}, note = {cite arxiv:1610.07662}, timestamp = {2019-09-19T13:35:56.000+0200}, title = {A New Class of Private Chi-Square Tests}, url = {http://arxiv.org/abs/1610.07662}, year = 2016 }