2021/04/05 by Jinshuo Dong, Aaron Roth, Dong, Jinshuo +4 · 1 citation
Computer Science · Mathematics · Social Sciences · #Computer science #Cryptography and Data Security #Cryptography and Security (cs.CR) #Data mining #Differential (mechanical device) #Differential privacy #FOS: Computer and information sciences #FOS: Mathematics #Gaussian #Internet privacy #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Physics #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Quantum mechanics #Statistics Theory (math.ST) #Thermodynamics #cs.CR #cs.LG #math.ST #stat.ML #stat.TH
paper · pdf · doi:10.48550/arxiv.2104.01987
Updated the references. Rejoinder to discussions on Gaussian Differential Privacy, read to the Royal Statistical Society in December 2020
openalex publication_date 2021/04/05 · arxiv created 2021/06/26 · arxiv updated 2021/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.