Differentially Private Inference for Binomial Data

@article{Awan2019DifferentiallyPI,
  title={Differentially Private Inference for Binomial Data},
  author={Jordan Awan and Aleksandra B. Slavkovic},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.00459}
}
  • Jordan Awan, Aleksandra B. Slavkovic
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance. We show that in general, DP hypothesis tests can be written in terms of linear constraints, and for exchangeable data can always be expressed as a function of the empirical distribution. Using this structure, we prove a 'Neyman-Pearson lemma' for binomial data under DP, where the DP-UMP only depends… CONTINUE READING

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