28 citations · 33 across the 5 of their papers we have counts for
5 papers
Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications
Yong Yang, Chong Fu, Tong Zhang +6
Large language model (LLM)-based applications rely on system prompts to encode core logic and developer-defined constraints, making these prompts important intellectual property. H…
Semantic Encryption: Secure and Effective Interaction with Cloud-based Large Language Models via Semantic Transformation
Dong Chen, Tong Yang, Feipeng Zhai +5
The increasing adoption of Cloud-based Large Language Models (CLLMs) has raised significant concerns regarding data privacy during user interactions. While existing approaches prim…
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Chong Fu, Xuhong Zhang, Shouling Ji +4
Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs.…
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
Pengyu Qiu, Xuhong Zhang, Shouling Ji +3
Vertical Federated Learning (VFL) is a trending collaborative machine learning model training solution. Existing industrial frameworks employ secure multi-party computation techniq…
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings
Yuhao Mao, Chong Fu, Saizhuo Wang +7
One intriguing property of adversarial attacks is their "transferability" -- an adversarial example crafted with respect to one deep neural network (DNN) model is often found effec…