Transfer Learning for Human & AI

In this rejoinder, we aim to address two broad issues that cover most comments made in the discussion . First, we discuss some theoretical aspects ofour work and comment on how this work might impact the theoretical foundation of privacy-preserving data analysis . Taking a practical viewpoint, we nextdiscuss how f-differential privacy (f-DP) and Gaussian differential privacy (GDP) can make a difference in a range of applications .

Author(s) : Jinshuo Dong, Aaron Roth, Weijie J. Su

Links : PDF - Abstract

Code :


Keywords : privacy - differential - gaussian - theoretical - rejoinder -

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