4 papers
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
Haichao Sha, Ruixuan Liu, Yixuan Liu +1
The paradigm of Differentially Private SGD~(DP-SGD) can provide a theoretical guarantee for training data in both centralized and federated settings. However, the utility degradati…
Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise
Yuhan Liu, Sheng Wang, Yixuan Liu +2
The Sparse Vector Technique (SVT) is one of the most fundamental tools in differential privacy (DP). It works as a backbone for adaptive data analysis by answering a sequence of qu…
Enhanced Privacy Bound for Shuffle Model with Personalized Privacy
Yixuan Liu, Yuhan Liu, Li Xiong +2
The shuffle model of Differential Privacy (DP) is an enhanced privacy protocol which introduces an intermediate trusted server between local users and a central data curator. It si…
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
Yixuan Liu, Li Xiong, Yuhan Liu +3
Differentially Private Stochastic Gradients Descent (DP-SGD) is a prominent paradigm for preserving privacy in deep learning. It ensures privacy by perturbing gradients with random…