Cover Image

CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Weiqi, Zhang, Chenhan, Tian, Zhiyi, Liu, Shushu, Yu, Shui
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine Unlearning: A Comprehensive Survey
by: Wang, Weiqi, et al.
Published: (2024)

SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
by: Wang, Weiqi, et al.
Published: (2025)

BlindU: Blind Machine Unlearning without Revealing Erasing Data
by: Wang, Weiqi, et al.
Published: (2026)

TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning
by: Wang, Weiqi, et al.
Published: (2025)

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

Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation
by: Chen, Luoyu, et al.
Published: (2026)

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
by: Fu, Jie, et al.
Published: (2026)

Ellipsoid Control: A White-list Jailbreak Defense via Benign Latent Modeling
by: Chen, Luoyu, et al.
Published: (2026)

How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks
by: P., Muhammed Shafi K., et al.
Published: (2025)

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

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

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

Label Leakage Attacks in Machine Unlearning: A Parameter and Inversion-Based Approach
by: Zheng, Weidong, et al.
Published: (2026)

A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
by: Hu, Hongsheng, et al.
Published: (2023)

Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
by: Hu, Hongsheng, et al.
Published: (2024)

Computing Maximal Per-Record Leakage and Leakage-Distortion Functions for Privacy Mechanisms under Entropy-Constrained Adversaries
by: Wu, Genqiang, et al.
Published: (2026)

ACU: Analytic Continual Unlearning for Efficient and Exact Forgetting with Privacy Preservation
by: Tang, Jianheng, et al.
Published: (2025)

SMS: Self-supervised Model Seeding for Verification of Machine Unlearning
by: Wang, Weiqi, et al.
Published: (2025)

RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage
by: Zhong, Peter Yong, et al.
Published: (2025)

A Framework to Prevent Biometric Data Leakage in the Immersive Technologies Domain
by: Sood, Keshav, et al.
Published: (2025)

Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents
by: Ye, Dayong, et al.
Published: (2026)

PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning
by: Li, Xiangman, et al.
Published: (2025)

S-Leak: Leakage-Abuse Attack Against Efficient Conjunctive SSE via s-term Leakage
by: Su, Yue, et al.
Published: (2025)

Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration
by: Zuo, Xuhan, et al.
Published: (2024)

Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
by: Zuo, Xuhan, et al.
Published: (2024)

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)

When Forgetting Triggers Backdoors: A Clean Unlearning Attack
by: Arazzi, Marco, et al.
Published: (2025)

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage
by: Li, Na, et al.
Published: (2025)

Privacy-Preserving Federated Unlearning with Certified Client Removal
by: Liu, Ziyao, et al.
Published: (2024)

Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)

When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
by: Wang, Shang, et al.
Published: (2024)

DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
by: Hmida, Firas Ben, et al.
Published: (2026)

Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection
by: Ferdous, Jannatul, et al.
Published: (2026)

Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation
by: Liu, Ziyao, et al.
Published: (2024)

Machine Unlearning in Large Language Models
by: Chen, Kongyang, et al.
Published: (2024)

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

Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
by: Hardan, Shahad, et al.
Published: (2025)

ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models
by: Xu, Zhenhua, et al.
Published: (2026)

Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget
by: Jin, Jiankai, et al.
Published: (2024)

Leakage-abuse Attack Against Substring-SSE with Partially Known Dataset
by: Ba, Xijie, et al.
Published: (2025)