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Rejoinder: Gaussian Differential Privacy
Published 5 Apr 2021 in cs.CR, cs.LG, math.ST, stat.ML, and stat.TH | (2104.01987v2)
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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