BibTeX

@article{2310.12781v4,
Author        = {Yifei Xiong and Nianqiao Phyllis Ju and Sanguo Zhang},
Title         = {Simulation-based Bayesian Inference from Privacy Protected Data},
Eprint        = {2310.12781v4},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Many modern statistical analysis and machine learning applications require
training models on sensitive user data. Under a formal definition of privacy
protection, differentially private algorithms inject calibrated noise into the
confidential data or during the data analysis process to produce
privacy-protected datasets or queries. However, restricting access to only
privatized data during statistical analysis makes it computationally
challenging to make valid statistical inferences. In this work, we propose
simulation-based inference methods from privacy-protected datasets. In addition
to sequential Monte Carlo approximate Bayesian computation, we adopt neural
conditional density estimators as a flexible family of distributions to
approximate the posterior distribution of model parameters given the observed
private query results. We illustrate our methods on discrete time-series data
under an infectious disease model and with ordinary linear regression models.
Illustrating the privacy-utility trade-off, our experiments and analysis
demonstrate the necessity and feasibility of designing valid statistical
inference procedures to correct for biases introduced by the privacy-protection
mechanisms.},
Year          = {2023},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2310.12781v4},
File          = {2310.12781v4.pdf}
}