3 papers
cs.LG2025
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…
cs.CV2024
Intermediate Outputs Are More Sensitive Than You Think
Tao Huang, Qingyu Huang, Jiayang Meng
The increasing reliance on deep computer vision models that process sensitive data has raised significant privacy concerns, particularly regarding the exposure of intermediate resu…
cs.LG2024
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
Tao Huang, Qingyu Huang, Xin Shi +4
In the domain of deep learning, the challenge of protecting sensitive data while maintaining model utility is significant. Traditional Differential Privacy (DP) techniques such as…