Rejoinder: Gaussian Differential Privacy
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 5 Apr 2021.
Builds on
8 of 14 references.
References · 8
- 2016Deep Learning with Differential Privacycited by 2,728
- 2019Deep Learning with Gaussian Differential Privacycited by 114
- 2021Differentially Private Query Release Through Adaptive Projectioncited by 52
- 2020New Oracle-Efficient Algorithms for Private Synthetic Data Releasecited by 43
- 2021Differentially private inference via noisy optimizationcited by 27
- 2014Dual Query: Practical Private Query Release for High Dimensional Datacited by 26
- 2020Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansioncited by 13
- 2021A Central Limit Theorem for Differentially Private Query Answeringcited by 7
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.