Rejoinder: Gaussian Differential Privacy
Jinshuo Dong, Aaron Roth, Weijie J. Su
TL;DR
This paper discusses the theoretical foundations and practical applications of Gaussian differential privacy (GDP) and f-differential privacy (f-DP), highlighting their impact on privacy-preserving data analysis.
Contribution
It provides a theoretical discussion on GDP and f-DP and explores their practical significance across various applications.
Findings
GDP enhances privacy guarantees in data analysis
f-DP offers flexible privacy trade-offs
Theoretical insights inform practical privacy implementations
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.
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Privacy, Security, and Data Protection