13 papers
Cost-Efficient Privacy Enforcement for Email-Generating LLM Agents under Strategic Information Requests
Qikun Cai, Tao Huang, Chen Hou
LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf of a user. Existing checker-re…
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…
CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents
Tao Huang, Guosen Wu, Guolong Zheng +5
Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval, or tool-use pipelines, which makes it difficult to distinguish internal…
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…
PAPC: Platform Mediation for Privacy-Propagation Externalities in AI-Mediated Workflows
Tao Huang, Guosen Wu, Chen Hou +1
AI-mediated platforms coordinate work through LLM agents acting for different principals. In these workflows, privacy loss can be created before a final answer appears: a memory wr…
Semantic-TVM: Structure-Preserving Trustworthy Virtual Memory for Memory-Augmented and Tool-Using Agents
Yu Li, Qikun Cai, Tao Huang +1
Memory-augmented and tool-using agents expose exact private values when remote LLMs process retrieved memory, tool actions, and intermediate observations. One-way masking limits di…
Probing into Camera Control of Video Models
Chen Hou, Christian Rupprecht
Video is a rich and scalable source of 3D/4D visual observations, and camera control is a key capability for video generation models to produce geometrically meaningful content. Ex…
Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
Tao Huang, Chen Hou, Guosen Wu +1
Privacy leakage in LLM agents is often studied through individual storage or execution components, such as memory modules, retrieval pipelines, or tool-mediated artifacts. However,…
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…
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…