6 papers · 1 filter
Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting
Guoxiong Long, Huizhen Huang, Qikun Cai +2
Differentially private time-series generators commonly produce fixed-length synthetic windows, whereas downstream forecasting models often require long continuous training sequence…
Energy Time-Series Imputation with Differentially Private Diffusion Models via Clipping-Aware Objective Conditioning
Huizhen Huang, Yu Li, Tao Huang +1
Reliable recovery of missing measurements is important for monitoring and analysis in energy time-series systems, where fine-grained measurements may contain sensitive temporal inf…
A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters
Hongkai Zhuang, Tao Huang, Chen Hou
Covariate-rich time-series forecasting requires deciding how external variables enter the target forecasting path. Existing Transformer-based forecasters usually build a covariate…
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…
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…
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…