Cover Image

Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chang, Wenhan, Zhu, Tianqing, Xu, Heng, Liu, Wenjian, Zhou, Wanlei
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
by: Chang, Wenhan, et al.
Published: (2024)

Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
by: Chen, Huiqiang, et al.
Published: (2025)

Machine Unlearning via Null Space Calibration
by: Chen, Huiqiang, et al.
Published: (2024)

Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
by: Xu, Heng, et al.
Published: (2024)

Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
by: Xu, Heng, et al.
Published: (2024)

Forgetting Similar Samples: Can Machine Unlearning Do it Better?
by: Xu, Heng, et al.
Published: (2026)

Vertical Federated Unlearning via Backdoor Certification
by: Han, Mengde, et al.
Published: (2024)

Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
by: Qin, Laiqiao, et al.
Published: (2024)

Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
by: Xu, Heng, et al.
Published: (2024)

Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications
by: Xu, Heng, et al.
Published: (2024)

Graph Unlearning: Efficient Node Removal in Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2025)

Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs
by: Wang, Linlin, et al.
Published: (2025)

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)

Decoupling the Class Label and the Target Concept in Machine Unlearning
by: Zhu, Jianing, et al.
Published: (2024)

Don't Forget Too Much: Towards Machine Unlearning on Feature Level
by: Xu, Heng, et al.
Published: (2024)

Reinforcement Unlearning
by: Ye, Dayong, et al.
Published: (2023)

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
by: Tian, Huan, et al.
Published: (2023)

Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
by: Wu, Yongliang, et al.
Published: (2024)

Defending Against Neural Network Model Inversion Attacks via Data Poisoning
by: Zhou, Shuai, et al.
Published: (2024)

Machine Unlearning Fails to Remove Data Poisoning Attacks
by: Pawelczyk, Martin, et al.
Published: (2024)

AFed: Algorithmic Fair Federated Learning
by: Chen, Huiqiang, et al.
Published: (2025)

Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage
by: Liu, Hengzhu, et al.
Published: (2024)

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
by: Qin, Laiqiao, et al.
Published: (2024)

Poisoning Bayesian Inference via Data Deletion and Replication
by: Carreau, Matthieu, et al.
Published: (2025)

Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
by: Wang, Linlin, et al.
Published: (2024)

Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers
by: Tian, Huan, et al.
Published: (2025)

Osmosis Distillation: Model Hijacking with the Fewest Samples
by: Shi, Yuchen, et al.
Published: (2026)

Safe and Reliable Diffusion Models via Subspace Projection
by: Chen, Huiqiang, et al.
Published: (2025)

On the Limitations and Prospects of Machine Unlearning for Generative AI
by: Zhou, Shiji, et al.
Published: (2024)

Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
by: Di, Jimmy Z., et al.
Published: (2022)

Towards Aligned Data Forgetting via Twin Machine Unlearning
by: Niu, Zhenxing, et al.
Published: (2025)

Potion: Towards Poison Unlearning
by: Schoepf, Stefan, et al.
Published: (2024)

ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
by: Lin, Shen, et al.
Published: (2026)

Distributional Machine Unlearning via Selective Data Removal
by: Allouah, Youssef, et al.
Published: (2025)

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
by: Zhang, Guangsheng, et al.
Published: (2022)

Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy
by: Sosnin, Philip, et al.
Published: (2025)

Beyond Uniform Deletion: A Data Value-Weighted Framework for Certified Machine Unlearning
by: He, Lisong, et al.
Published: (2025)

Large Language Models for Link Stealing Attacks Against Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2024)

Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
by: Zhou, Shiji, et al.
Published: (2025)

Data Augmentation Improves Machine Unlearning
by: Falcao, Andreza M. C., et al.
Published: (2025)