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.
- Pub Date:
- April 2021
- Computer Science - Cryptography and Security;
- Computer Science - Machine Learning;
- Mathematics - Statistics Theory;
- Statistics - Machine Learning
- Updated the references. Rejoinder to discussions on Gaussian Differential Privacy, read to the Royal Statistical Society in December 2020