Simulation-based, Finite-sample Inference for Privatized Data

arXiv:2303.05328v4 Announce Type: replace-cross
Abstract: Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based "repro sample" approach to produce statistically valid confidence intervals and hypothesis tests, which builds on the work of Xie and Wang (2022). We show that this methodology is applicable to a wide variety of private inference problems, appropriately accounts for biases introduced by privacy mechanisms (such as …

arxiv cs.cr data distributions math.st noise privacy private protection sample simulation statistics stat.me stat.th tests valid wang work