4 papers
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…
Clip Body and Tail Separately: High Probability Guarantees for DPSGD with Heavy Tails
Haichao Sha, Yang Cao, Yong Liu +3
Differentially Private Stochastic Gradient Descent (DPSGD) is widely utilized to preserve training data privacy in deep learning, which first clips the gradients to a predefined no…