10 papers
ECHO: Replacing the Compliance Proxy with Response Harmfulness in Optimization-Based Jailbreak Attack
Yuanwei Wu, Lingdong Huang, Yunlong Mao +3
Despite extensive safety alignment, large language models remain vulnerable to jailbreak attacks. Existing optimization-based attacks share a single objective: maximize a token-lev…
Gradient Mirage: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning
Shiyu Miao, Yunlong Mao, Zirui Huang +5
Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface is a faithful derivative of…
Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints
Zirui Huang, Yunlong Mao, Wei Tong +3
The proliferation of customized Large Language Models (LLMs) poses critical risks of Data Intellectual Property (Data IP) infringement via unauthorized fine-tuning on proprietary d…
Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
Zi Li, Tian Zhou, Wenze Li +3
Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although "local offline fine-tuning" is often viewed a…
Towards Privacy-Preserving LLM Inference via Covariant Obfuscation (Technical Report)
Yu Lin, Qizhi Zhang, Wenqiang Ruan +6
The rapid development of large language models (LLMs) has driven the widespread adoption of cloud-based LLM inference services, while also bringing prominent privacy risks associat…
On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
Zijian Wang, Wei Tong, Tingxuan Han +4
Federated learning (FL) combined with local differential privacy (LDP) enables privacy-preserving model training across decentralized data sources. However, the decentralized data-…
SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning
Chengcheng Zhu, Ye Li, Bosen Rao +3
Federated Learning (FL) has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenge…
OFL: Opportunistic Federated Learning for Resource-Heterogeneous and Privacy-Aware Devices
Yunlong Mao, Mingyang Niu, Ziqin Dang +7
Efficient and secure federated learning (FL) is a critical challenge for resource-limited devices, especially mobile devices. Existing secure FL solutions commonly incur significan…
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation
Ying He, Mingyang Niu, Jingyu Hua +4
Split Neural Network, as one of the most common architectures used in vertical federated learning, is popular in industry due to its privacy-preserving characteristics. In this arc…
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks
Yunlong Mao, Zexi Xin, Zhenyu Li +3
Split learning of deep neural networks (SplitNN) has provided a promising solution to learning jointly for the mutual interest of a guest and a host, which may come from different…