Auditing Approximate Machine Unlearning for Differentially Private Models
Fuente:
arXiv
Saved in:
| Main Authors: | Gu, Yuechun, He, Jiajie, Chen, Keke |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
RecPS: Privacy Risk Scoring for Recommender Systems
by: He, Jiajie, et al.
Published: (2025)
by: He, Jiajie, et al.
Published: (2025)
Adaptive Domain Inference Attack with Concept Hierarchy
by: Gu, Yuechun, et al.
Published: (2023)
by: Gu, Yuechun, et al.
Published: (2023)
Membership Inference Attacks on LLM-based Recommender Systems
by: He, Jiajie, et al.
Published: (2025)
by: He, Jiajie, et al.
Published: (2025)
Efficient Machine Unlearning via Influence Approximation
by: Liu, Jiawei, et al.
Published: (2025)
by: Liu, Jiawei, et al.
Published: (2025)
Towards Differentially Private Reinforcement Learning with General Function Approximation
by: He, Yi, et al.
Published: (2026)
by: He, Yi, et al.
Published: (2026)
Auditing Language Model Unlearning via Information Decomposition
by: Goel, Anmol, et al.
Published: (2026)
by: Goel, Anmol, et al.
Published: (2026)
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
by: Yuan, Chih-Cheng Rex, et al.
Published: (2025)
by: Yuan, Chih-Cheng Rex, et al.
Published: (2025)
Governing AI Forgetting: Auditing for Machine Unlearning Compliance
by: Lin, Qinqi, et al.
Published: (2026)
by: Lin, Qinqi, et al.
Published: (2026)
Machine Unlearning in Contrastive Learning
by: Wang, Zixin, et al.
Published: (2024)
by: Wang, Zixin, et al.
Published: (2024)
On the Sample Complexity of Differentially Private Policy Optimization
by: He, Yi, et al.
Published: (2025)
by: He, Yi, et al.
Published: (2025)
Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)
by: Cadet, Xavier F., et al.
Published: (2024)
Feature-Selective Representation Misdirection for Machine Unlearning
by: Chen, Taozhao, et al.
Published: (2025)
by: Chen, Taozhao, et al.
Published: (2025)
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
by: Hsu, Hsiang, et al.
Published: (2026)
by: Hsu, Hsiang, et al.
Published: (2026)
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
by: Wang, Bin, et al.
Published: (2026)
by: Wang, Bin, et al.
Published: (2026)
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
by: Han, Ling, et al.
Published: (2024)
by: Han, Ling, et al.
Published: (2024)
Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canaries
by: Li, Yanhang, et al.
Published: (2026)
by: Li, Yanhang, et al.
Published: (2026)
Debiasing Machine Unlearning with Counterfactual Examples
by: Chen, Ziheng, et al.
Published: (2024)
by: Chen, Ziheng, et al.
Published: (2024)
Machine Unlearning via Null Space Calibration
by: Chen, Huiqiang, et al.
Published: (2024)
by: Chen, Huiqiang, et al.
Published: (2024)
Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)
by: Block, Jacob L., et al.
Published: (2025)
Group-robust Machine Unlearning
by: De Min, Thomas, et al.
Published: (2025)
by: De Min, Thomas, et al.
Published: (2025)
Soft Weighted Machine Unlearning
by: Qiao, Xinbao, et al.
Published: (2025)
by: Qiao, Xinbao, et al.
Published: (2025)
GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints
by: Zhu, Andy, et al.
Published: (2026)
by: Zhu, Andy, et al.
Published: (2026)
Machine Unlearning: Solutions and Challenges
by: Xu, Jie, et al.
Published: (2023)
by: Xu, Jie, et al.
Published: (2023)
A Survey of Machine Unlearning
by: Nguyen, Thanh Tam, et al.
Published: (2022)
by: Nguyen, Thanh Tam, et al.
Published: (2022)
Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
by: Gu, Kang, et al.
Published: (2024)
by: Gu, Kang, et al.
Published: (2024)
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
by: Huang, Zhehao, et al.
Published: (2024)
by: Huang, Zhehao, et al.
Published: (2024)
The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models
by: Xiao, Yang, et al.
Published: (2025)
by: Xiao, Yang, et al.
Published: (2025)
Differentially Private Model Merging
by: Yin, Qichuan, et al.
Published: (2026)
by: Yin, Qichuan, et al.
Published: (2026)
EVE: Efficient Verification of Data Erasure through Customized Perturbation in Approximate Unlearning
by: Wang, Weiqi, et al.
Published: (2026)
by: Wang, Weiqi, et al.
Published: (2026)
SIMU: Selective Influence Machine Unlearning
by: Agarwal, Anu, et al.
Published: (2025)
by: Agarwal, Anu, et al.
Published: (2025)
Data Augmentation Improves Machine Unlearning
by: Falcao, Andreza M. C., et al.
Published: (2025)
by: Falcao, Andreza M. C., et al.
Published: (2025)
An Information Theoretic Approach to Machine Unlearning
by: Foster, Jack, et al.
Published: (2024)
by: Foster, Jack, et al.
Published: (2024)
Verifying Machine Unlearning with Explainable AI
by: Vidal, Àlex Pujol, et al.
Published: (2024)
by: Vidal, Àlex Pujol, et al.
Published: (2024)
Towards Reliable Testing of Machine Unlearning
by: Mazhar, Anna, et al.
Published: (2026)
by: Mazhar, Anna, et al.
Published: (2026)
Does Machine Unlearning Truly Remove Knowledge?
by: Chen, Haokun, et al.
Published: (2025)
by: Chen, Haokun, et al.
Published: (2025)
Membership Inference Attacks on Recommender System: A Survey
by: He, Jiajie, et al.
Published: (2025)
by: He, Jiajie, et al.
Published: (2025)
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Approximate Domain Unlearning for Vision-Language Models
by: Kawamura, Kodai, et al.
Published: (2025)
by: Kawamura, Kodai, et al.
Published: (2025)
Similar Items
-
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024) -
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
by: Gu, Yuechun, et al.
Published: (2024) -
RecPS: Privacy Risk Scoring for Recommender Systems
by: He, Jiajie, et al.
Published: (2025) -
Adaptive Domain Inference Attack with Concept Hierarchy
by: Gu, Yuechun, et al.
Published: (2023) -
Membership Inference Attacks on LLM-based Recommender Systems
by: He, Jiajie, et al.
Published: (2025)