Rejoinder: Gaussian Differential Privacy
arXiv:2104.01987v23 citations
AI Analysis
This work is incremental, refining privacy frameworks for data analysis.
The authors addressed theoretical and practical aspects of f-differential privacy and Gaussian differential privacy, discussing their impact on privacy-preserving data analysis foundations and applications.
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.