most citedAn LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection

9 citations · 10 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CR2025

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective

Nima Naderloui, Shenao Yan, Binghui Wang +4

Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…

cs.LG2024★ 1 cited

Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence

Shuya Feng, Meisam Mohammady, Hanbin Hong +4

Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tra…

cs.CR2024★ 9 cited

An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection

Shenao Yan, Shen Wang, Yue Duan +4

Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fin…