18 citations · 61 across the 51 of their papers we have counts for
19 papers · 1 filter
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
Heng Xu, Tianqing Zhu, Dayong Ye +3
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extens…
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…
Vertical Federated Unlearning via Backdoor Certification
Mengde Han, Tianqing Zhu, Lefeng Zhang +2
Vertical Federated Learning (VFL) offers a novel paradigm in machine learning, enabling distinct entities to train models cooperatively while maintaining data privacy. This method…
Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
Laiqiao Qin, Tianqing Zhu, Linlin Wang +1
Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting the model performance on the remaining…
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
Wenhan Chang, Tianqing Zhu, Ping Xiong +3
In the rapid advancement of artificial intelligence, privacy protection has become crucial, giving rise to machine unlearning. Machine unlearning is a technique that removes specif…
Large Language Models for Link Stealing Attacks Against Graph Neural Networks
Faqian Guan, Tianqing Zhu, Hui Sun +2
Graph data contains rich node features and unique edge information, which have been applied across various domains, such as citation networks or recommendation systems. Graph Neura…
Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, ha…
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
Linlin Wang, Tianqing Zhu, Wanlei Zhou +1
Federated learning is fast becoming a popular paradigm for applications involving mobile devices, banking systems, healthcare, and IoT systems. Hence, over the past five years, res…
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
Laiqiao Qin, Tianqing Zhu, Wanlei Zhou +1
Federated Learning (FL) is a distributed and privacy-preserving machine learning paradigm that coordinates multiple clients to train a model while keeping the raw data localized. H…
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security
Youyang Qu, Ming Liu, Tianqing Zhu +3
Federated Learning (FL) offers a promising paradigm for training Large Language Models (LLMs) in a decentralized manner while preserving data privacy and minimizing communication o…
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
Wenhan Chang, Tianqing Zhu, Heng Xu +2
Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This capability is particularly impo…
Machine Unlearning via Null Space Calibration
Huiqiang Chen, Tianqing Zhu, Xin Yu +1
Machine unlearning aims to enable models to forget specific data instances when receiving deletion requests. Current research centres on efficient unlearning to erase the influence…
When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
Huan Tian, Guangsheng Zhang, Bo Liu +3
Previous studies have developed fairness methods for biased models that exhibit discriminatory behaviors towards specific subgroups. While these models have shown promise in achiev…
Generative Adversarial Networks Unlearning
Hui Sun, Tianqing Zhu, Wenhan Chang +1
As machine learning continues to develop, and data misuse scandals become more prevalent, individuals are becoming increasingly concerned about their personal information and are a…
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Huiqiang Chen, Tianqing Zhu, Tao Zhang +2
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair res…