@InProceedings{rogers16,
  supplementary = {Supplementary:rogers16-supp.pdf},
  title = {Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing},
  author = {Ryan Rogers and Salil Vadhan and Hyun Lim and Marco Gaboardi},
  pages = {2111-2120},
  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.},
}

@InProceedings{rogers16,
  supplementary = {Supplementary:rogers16-supp.pdf},
  title = {Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing},
  author = {Marco Gaboardi and Hyun Lim and Ryan Rogers and Salil Vadhan},
  pages = {2111-2120},
  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.},
}

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