REVIEW 9 cited by
Certified Data Removal from Machine Learning Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.
Forward citations
Cited by 9 Pith papers
-
Unlearning as Distribution Restoration: A Controlled Counterfactual Study, a Validated Selective Screen, and the Limits of Oracle-Free Certification
Matching a retrained oracle on trained probes can certify models that still retain held-out forget knowledge, and oracle-free unlearning certification is only possible for counterfactual, non-inferable facts.
-
Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
Water4MU tunes an invisible watermark on data so that machine unlearning algorithms can remove requested images more effectively, beating prior methods on 'challenging forgets'.
-
LoReUn: Data Itself Implicitly Provides Cues to Improve Machine Unlearning
LoReUn, a plug-in loss-based reweighting strategy, improves approximate machine unlearning by focusing updates on hard-to-forget low-loss data points.
-
How to Protect Models against Adversarial Unlearning?
A 'healing' procedure that uses similar real examples as surrogates for unlearned data during fine-tuning mitigates accuracy loss from machine unlearning in several classification benchmarks.
-
LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats
Most gradient-based LLM unlearning methods achieve behavioral suppression, not true forgetting, and current benchmarks cannot certify that knowledge has been removed.
-
Membership Inference Attacks with False Discovery Rate Control
A post-hoc wrapper, MIAFdR, converts any membership inference attack scores into conformal p-values and applies a Benjamini-Hochberg correction, guaranteeing that the expected proportion of non-members among flagged m...
-
Leveraging Distribution Matching to Make Approximate Machine Unlearning Faster
A dual data and loss-centric method claims to speed up machine unlearning, but its MIA regularizer cancels itself and the test set is leaked into training.
-
MRD-LiNet: A Novel Lightweight Hybrid CNN with Gradient-Guided Unlearning for Improved Drought Stress Identification
A 0.231M-parameter CNN achieves 90.0% accuracy on potato drought stress detection, and removing 5% of low-gradient-score training samples improves accuracy from 88.6% to 90.0%.
-
Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning
FAMR uses a uniform-prediction KL loss plus an L2 anchor to original weights for class unlearning, but the theory is mis-derived and the method is a known variant of zero-shot unlearning.
Discussion (0). Sign in to comment.