9 citations · 19 across the 19 of their papers we have counts for
7 papers · 1 filter
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
Zhenyu Wen, Wanglei Feng, Di Wu +6
Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Altho…
CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
Rui Zeng, Xi Chen, Yuwen Pu +3
Backdoors can be injected into NLP models to induce misbehavior when the input text contains a specific feature, known as a trigger, which the attacker secretly selects. Unlike fix…
Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting
Tong Ye, Yangkai Du, Tengfei Ma +4
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating code. However, the misuse of LLM-generated (synthetic) code has raised concerns in both educatio…
An Inversion-based Measure of Memorization for Diffusion Models
Zhe Ma, Qingming Li, Xuhong Zhang +5
The past few years have witnessed substantial advances in image generation powered by diffusion models. However, it was shown that diffusion models are susceptible to training data…
Marlin: Knowledge-Driven Analysis of Provenance Graphs for Efficient and Robust Detection of Cyber Attacks
Zhenyuan Li, Yangyang Wei, Xiangmin Shen +9
Recent research in both academia and industry has validated the effectiveness of provenance graph-based detection for advanced cyber attack detection and investigation. However, an…
PRSA: Prompt Stealing Attacks against Real-World Prompt Services
Yong Yang, Changjiang Li, Qingming Li +6
Recently, large language models (LLMs) have garnered widespread attention for their exceptional capabilities. Prompts are central to the functionality and performance of LLMs, maki…
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness
Mengyao Du, Miao Zhang, Yuwen Pu +3
To tackle the scarcity and privacy issues associated with domain-specific datasets, the integration of federated learning in conjunction with fine-tuning has emerged as a practical…