Enhanced Privacy Bound for Shuffle Model with Personalized Privacy
Yixuan Liu, Yuhan Liu, Li Xiong, Yujie Gu, Hong Chen · 2024
-DP, where the convexity of the distributions is leveraged to achieve a tighter privacy bound. Theoretical and numerical results demonstrate that our bound remarkably outperforms the existing results in the literature. The code is publicly available at https://github.com/Emory-AIMS/HPS.git.