Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

@InProceedings{pmlr-v48-rogers16,
  title = 	 {Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing},
  author = 	 {Gaboardi, Marco and Lim, Hyun and Rogers, Ryan and Vadhan, Salil},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {2111--2120},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/rogers16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/rogers16.html},
  abstract = 	 {Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables.}
}