Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Heng, Zhu, Tianqing, Zhang, Lefeng, Zhou, Wanlei, Zhao, Wei |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
by: Xu, Heng, et al.
Published: (2026)
by: Xu, Heng, et al.
Published: (2026)
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
Don't Forget Too Much: Towards Machine Unlearning on Feature Level
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)
by: Zuo, Xuhan, et al.
Published: (2024)
Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
Reinforcement Unlearning
by: Ye, Dayong, et al.
Published: (2023)
by: Ye, Dayong, et al.
Published: (2023)
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
by: Chang, Wenhan, et al.
Published: (2024)
by: Chang, Wenhan, et al.
Published: (2024)
Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
by: Zuo, Xuhan, et al.
Published: (2024)
by: Zuo, Xuhan, et al.
Published: (2024)
Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
by: Qi, Minfeng, et al.
Published: (2025)
by: Qi, Minfeng, et al.
Published: (2025)
QUEEN: Query Unlearning against Model Extraction
by: Chen, Huajie, et al.
Published: (2024)
by: Chen, Huajie, et al.
Published: (2024)
Turning Black Box into White Box: Dataset Distillation Leaks
by: Chen, Huajie, et al.
Published: (2026)
by: Chen, Huajie, et al.
Published: (2026)
Osmosis Distillation: Model Hijacking with the Fewest Samples
by: Shi, Yuchen, et al.
Published: (2026)
by: Shi, Yuchen, et al.
Published: (2026)
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
by: Wang, Shang, et al.
Published: (2024)
by: Wang, Shang, et al.
Published: (2024)
Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs
by: Wang, Linlin, et al.
Published: (2025)
by: Wang, Linlin, 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)
by: Wang, Linlin, et al.
Published: (2024)
Towards Irreversible Machine Unlearning for Diffusion Models
by: Yuan, Xun, et al.
Published: (2025)
by: Yuan, Xun, et al.
Published: (2025)
Large Language Models for Link Stealing Attacks Against Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2024)
by: Guan, Faqian, et al.
Published: (2024)
When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
by: Tian, Huan, et al.
Published: (2023)
by: Tian, Huan, et al.
Published: (2023)
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)
by: Zhang, Guangsheng, et al.
Published: (2022)
Graph Unlearning with Efficient Partial Retraining
by: Zhang, Jiahao, et al.
Published: (2024)
by: Zhang, Jiahao, et al.
Published: (2024)
TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
by: Zhou, Chunyi, et al.
Published: (2024)
by: Zhou, Chunyi, et al.
Published: (2024)
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
by: Chen, Aobo, et al.
Published: (2026)
by: Chen, Aobo, et al.
Published: (2026)
Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration
by: Zuo, Xuhan, et al.
Published: (2024)
by: Zuo, Xuhan, et al.
Published: (2024)
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
by: Lee, Hong kyu, et al.
Published: (2024)
by: Lee, Hong kyu, et al.
Published: (2024)
Verification of Machine Unlearning is Fragile
by: Zhang, Binchi, et al.
Published: (2024)
by: Zhang, Binchi, et al.
Published: (2024)
SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
by: Wang, Weiqi, et al.
Published: (2025)
by: Wang, Weiqi, et al.
Published: (2025)
Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
by: Zheng, Weidong, et al.
Published: (2026)
by: Zheng, Weidong, et al.
Published: (2026)
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)
by: Zhang, Jiahao, et al.
Published: (2026)
Adversarial Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)
by: Di, Zonglin, et al.
Published: (2024)
Goldfish: An Efficient Federated Unlearning Framework
by: Wang, Houzhe, et al.
Published: (2024)
by: Wang, Houzhe, et al.
Published: (2024)
MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
by: Ye, Dayong, et al.
Published: (2025)
by: Ye, Dayong, et al.
Published: (2025)
Machine Unlearning for Traditional Models and Large Language Models: A Short Survey
by: Xu, Yi
Published: (2024)
by: Xu, Yi
Published: (2024)
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
by: Wang, Houzhe, et al.
Published: (2026)
by: Wang, Houzhe, et al.
Published: (2026)
Efficient Unlearning with Privacy Guarantees
by: Domingo-Ferrer, Josep, et al.
Published: (2025)
by: Domingo-Ferrer, Josep, et al.
Published: (2025)
Node-level Contrastive Unlearning on Graph Neural Networks
by: Lee, Hong kyu, et al.
Published: (2025)
by: Lee, Hong kyu, et al.
Published: (2025)
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
by: Zhou, Shuai, et al.
Published: (2024)
by: Zhou, Shuai, et al.
Published: (2024)
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)
by: Dilworth, Robert
Published: (2025)
Similar Items
-
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
by: Xu, Heng, et al.
Published: (2026) -
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
by: Xu, Heng, et al.
Published: (2024) -
Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
by: Xu, Heng, et al.
Published: (2024) -
Don't Forget Too Much: Towards Machine Unlearning on Feature Level
by: Xu, Heng, et al.
Published: (2024) -
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)