Topics In Differentially Private Statistical Inference

Loading...
Thumbnail Image
Degree type
Doctor of Philosophy (PhD)
Graduate group
Statistics
Discipline
Subject
Computer Sciences
Statistics and Probability
Funder
Grant number
License
Copyright date
2022-10-05T20:22:00-07:00
Distributor
Related resources
Author
Wang, Yichen
Contributor
Abstract

This dissertation studies the trade-off between differential privacy and statistical accuracy in parameter estimation problems. We understand the privacy-accuracy trade-off by finding the best achievable accuracy of any differentially private algorithm, also known as the "privacy-constrained minimax risk", in a series of statistical problems: Gaussian mean estimation and linear regression, estimation in general parametric models, and non-parametric function estimation. The increasing difficulty and generality of this series is matched by the development of differentially private algorithms such as noisy iterative hard thresholding, and of minimax lower bound techniques such as the score attack.

Advisor
T. Tony Cai
Date of degree
2022-01-01
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Recommended citation