6 papers
FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models
Abtin Mahyar, Masoumeh Shafieinejad, Yuhan Liu +1
Diffusion models are the leading approach for tabular data synthesis and are increasingly used to share sensitive records. Whether they actually protect privacy has become a pressi…
A Zeroth-order Resilient Algorithm for Distributed Online Optimization against Byzantine Edge Attacks
Yuhang Liu, Wenjun Mei
In this paper, we propose a zeroth-order resilient distributed online algorithm for networks under Byzantine edge attacks. We assume that both the edges attacked by Byzantine adver…
Sub-optimal Learning in Meta-Classifier Attacks: A Study of Membership Inference on Differentially Private Location Aggregates
Yuhan Liu, Florent Guepin, Igor Shilov +1
The widespread collection and sharing of location data, even in aggregated form, raises major privacy concerns. Previous studies used meta-classifier-based membership inference att…
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…