5 papers
Lilith: Backdoor Generalization under Training-Inference Trigger Shift
Zhou Feng, Jiahao Chen, Chunyi Zhou +6
The paper studies how backdoor attacks can remain effective when the trigger used at inference time differs from the one seen during training, and proposes Lilith, a black‑box meth…
Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem
Jiahao Chen, Xing He, Yong Yang +6
The prosperity of text-to-image (T2I) models has fostered a vibrant share-and-play ecosystem centered on Low-Rank Adaptation (LoRA) plugins, which allow users to customize and shar…
Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents
Jiahao Chen, Qi Zhang, Ruixiao Lin +7
Large Language Models (LLMs) have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion:…
DP-GENG : Differentially Private Dataset Distillation Guided by DP-Generated Data
Shuo Shi, Jinghuai Zhang, Shijie Jiang +5
Dataset distillation (DD) compresses large datasets into smaller ones while preserving the performance of models trained on them. Although DD is often assumed to enhance data priva…
LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping
Guanjie Cheng, Mengzhen Yang, Xinkui Zhao +5
Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient…