5 papers
Inverting the Hidden: Unveiling Multimodal Privacy Leakage in Collaborative LVLM Inference
Shuaifan Jin, Zhibo Wang, Qiyuan Wang +5
Collaborative inference deploys Large Vision-Language Models (LVLMs) by partitioning computation between edge devices and the cloud. While withholding raw inputs supposedly ensures…
Attention-Free and Lightweight Token Reduction for Efficient Vision-Language Models
Xuanyi Hao, Zuoyuan Zhang, Zhibo Wang +4
Vision-Language Models (VLMs) have achieved strong performance in multimodal understanding, yet remain challenging to deploy on resource-constrained edge devices due to the substan…
Privacy-Preserving LLM Embedding Transmission for End-Cloud Collaboration
Shuaifan Jin, Xiaoyi Pang, Zhibo Wang +4
Recent studies improve on-device language model (LM) inference through end-cloud collaboration, where the end device retrieves useful information from cloud databases to enhance lo…
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Zhibo Wang, Zhiwei Chang, Jiahui Hu +4
Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privac…
Textual Unlearning Gives a False Sense of Unlearning
Jiacheng Du, Zhibo Wang, Jie Zhang +3
Language Models (LMs) are prone to ''memorizing'' training data, including substantial sensitive user information. To mitigate privacy risks and safeguard the right to be forgotten…