3 citations · 6 across the 5 of their papers we have counts for
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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…
cs.CR2022★ 2 cited
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
Yi Liu, Ke Sun, Linglong Kong +1
Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of sensitive individual information. Des…