Cover Image

Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Hengzhu, Zhu, Tianqing, Zhang, Lefeng, Xiong, Ping
Format:	Preprint
Published:	2024
Subjects:	Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks
by: Liu, Hengzhu, et al.
Published: (2024)

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

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

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

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

TraceHiding: Scalable Machine Unlearning for Mobility Data
by: Faraji, Ali, et al.
Published: (2025)

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
by: Li, Na, et al.
Published: (2024)

A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
by: Lei, Songxin, et al.
Published: (2025)

Open Problems in Machine Unlearning for AI Safety
by: Barez, Fazl, et al.
Published: (2025)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
by: Cooper, A. Feder, et al.
Published: (2024)

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

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

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

Addressing Label Leakage in Knowledge Tracing Models
by: Badran, Yahya, et al.
Published: (2024)

Identifying Privacy Personas
by: Hrynenko, Olena, et al.
Published: (2024)

EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models
by: Li, Sichao, et al.
Published: (2024)

Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
by: Zhang, He, et al.
Published: (2023)

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

Machine Unlearning Meets Adversarial Robustness via Constrained Interventions on LLMs
by: Rezkellah, Fatmazohra, et al.
Published: (2025)

Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
by: Zhang, Yixuan, et al.
Published: (2024)

Training Fair Models in Federated Learning without Data Privacy Infringement
by: Che, Xin, et al.
Published: (2021)

Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering
by: Vijayan, Naveen Edapurath
Published: (2025)

Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems
by: Wang, Feilong, et al.
Published: (2024)

Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings
by: Badran, Yahya, et al.
Published: (2025)

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

Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
by: Rashid, Md Rafi Ur, et al.
Published: (2024)

An Ensemble Framework for Explainable Geospatial Machine Learning Models
by: Liu, Lingbo
Published: (2024)

Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation
by: Sun, Shuzhou, et al.
Published: (2025)

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
by: Li, Nathaniel, et al.
Published: (2024)

A Principled Approach for Data Bias Mitigation
by: Scarone, Bruno, et al.
Published: (2024)

Measuring and Mitigating Biases in Motor Insurance Pricing
by: Moriah, Mulah, et al.
Published: (2023)

Towards Public Administration Research Based on Interpretable Machine Learning
by: Liu, Zhanyu, et al.
Published: (2026)

Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
by: Chang, Wenhan, et al.
Published: (2024)

SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
by: Xu, Tianyang, et al.
Published: (2025)

Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data
by: Catarino, Andre, et al.
Published: (2025)

Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data
by: Ozeki, Ren, et al.
Published: (2024)

Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies
by: Hayat, Khizar, et al.
Published: (2025)

Toward Mitigating Sex Bias in Pilot Trainees' Stress and Fatigue Modeling
by: Pfeifer, Rachel, et al.
Published: (2024)

Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
by: Weber, Tobias, et al.
Published: (2023)