5 papers
Mitigating Membership Inference in Intermediate Representations with Differentially Private Training
Jiayang Meng, Tao Huang, Chen Hou +2
In Embedding-as-an-Interface (EaaI) settings, pre-trained models are queried for Intermediate Representations (IRs). The distributional properties of IRs can leak training-set memb…
DP-aware AdaLN-Zero: Taming Conditioning-Induced Heavy-Tailed Gradients in Differentially Private Diffusion
Tao Huang, Jiayang Meng, Xu Yang +2
Condition injection enables diffusion models to generate context-aware outputs, which is essential for many time-series tasks. However, heterogeneous conditional contexts (e.g., ob…
Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
Jiayang Meng, Tao Huang, Hong Chen +2
Federated learning synchronizes models through gradient transmission and aggregation. However, these gradients pose significant privacy risks, as sensitive training data is embedde…
Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
Jiayang Meng, Tao Huang, Hong Chen +3
The widespread deployment of deep learning models in privacy-sensitive domains has amplified concerns regarding privacy risks, particularly those stemming from gradient leakage dur…
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
Tao Huang, Jiayang Meng, Hong Chen +4
We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privac…