3 citations · 3 across the 3 of their papers we have counts for
3 papers
stat.ME2026
Differentially private inference framework for Riemannian manifold data
Yangdi Jiang, Xiaotian Chang, Qirui Hu
We propose a novel and systematic differentially private (DP) inference framework for non-Euclidean data. First, we design two types of DP mechanisms for the Fréchet mean and varia…
stat.ML2026
Geometric Renyi Differential Privacy: Ricci Curvature Characterized by Heat Diffusion Mechanisms
Xiaotian Chang, Yangdi Jiang, Cyrus Mostajeran +1
In this paper, we develop a novel privacy mechanism for Riemannian manifold-valued data. Our key contribution lies in uncovering unexpected connections among geometric analysis, he…
cs.CR2023★ 3 cited
Gaussian Differential Privacy on Riemannian Manifolds
Yangdi Jiang, Xiaotian Chang, Yi Liu +3
We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds. The concept of GDP stands out as a prominent privacy definition t…