18 citations · 61 across the 51 of their papers we have counts for
5 papers · 2 filters
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
Faqian Guan, Tianqing Zhu, Zhoutian Wang +2
With increasing concerns about privacy attacks and potential sensitive information leakage, researchers have actively explored methods to efficiently remove sensitive training data…
Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
Huiqiang Chen, Tianqing Zhu, Xin Yu +1
Machine unlearning aims to remove the influence of specific samples from a trained model. A key challenge in this process is over-unlearning, where the model's performance on the r…
Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs
Linlin Wang, Tianqing Zhu, Laiqiao Qin +2
In Large Language Models, Retrieval-Augmented Generation (RAG) systems can significantly enhance the performance of large language models by integrating external knowledge. However…
Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers
Huan Tian, Guangsheng Zhang, Bo Liu +3
While in-processing fairness approaches show promise in mitigating biased predictions, their potential impact on privacy leakage remains under-explored. We aim to address this gap…
AFed: Algorithmic Fair Federated Learning
Huiqiang Chen, Tianqing Zhu, Wanlei Zhou +1
Federated Learning (FL) has gained significant attention as it facilitates collaborative machine learning among multiple clients without centralizing their data on a server. FL ens…