A Differentially Private Kernel Two-Sample Test

  title={A Differentially Private Kernel Two-Sample Test},
  author={Anant Raj and Ho Chung Leon Law and D. Sejdinovic and Mijung Park},
Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those distributions. However, raw data samples can expose sensitive information about individuals who participate in scientific studies, which makes the current tests vulnerable to privacy breaches. Hence, we design a new framework for kernel two-sample testing conforming to differential privacy constraints, in order to… Expand
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