Rejoinder: Gaussian Differential Privacy
Cryptography and Security
2021-06-29 v2 Machine Learning
Statistics Theory
Machine Learning
Statistics Theory
Abstract
In this rejoinder, we aim to address two broad issues that cover most comments made in the discussion. First, we discuss some theoretical aspects of our work and comment on how this work might impact the theoretical foundation of privacy-preserving data analysis. Taking a practical viewpoint, we next discuss how f-differential privacy (f-DP) and Gaussian differential privacy (GDP) can make a difference in a range of applications.
Keywords
Cite
@article{arxiv.2104.01987,
title = {Rejoinder: Gaussian Differential Privacy},
author = {Jinshuo Dong and Aaron Roth and Weijie J. Su},
journal= {arXiv preprint arXiv:2104.01987},
year = {2021}
}
Comments
Updated the references. Rejoinder to discussions on Gaussian Differential Privacy, read to the Royal Statistical Society in December 2020