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Certifiable Machine Unlearning for Linear Models
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Machine unlearning is the task of updating machine learning (ML) models after a subset of the training data they were trained on is deleted. Methods for the task are desired to combine effectiveness and efficiency, i.e., they should effectively "unlearn" deleted data, but in a way that does not require excessive computation effort (e.g., a full retraining) for a small amount of deletions. Such a combination is typically achieved by tolerating some amount of approximation in the unlearning. In addition, laws and regulations in the spirit of "the right to be forgotten" have given rise to requirements for certifiability, i.e., the ability to demonstrate that the deleted data has indeed been unlearned by the ML model. In this paper, we present an experimental study of the three state-of-the-art approximate unlearning methods for linear models and demonstrate the trade-offs between efficiency, effectiveness and certifiability offered by each method. In implementing the study, we extend some of the existing works and describe a common ML pipeline to compare and evaluate the unlearning methods on six real-world datasets and a variety of settings. We provide insights into the effect of the quantity and distribution of the deleted data on ML models and the performance of each unlearning method in different settings. We also propose a practical online strategy to determine when the accumulated error from approximate unlearning is large enough to warrant a full retrain of the ML model.
Forward citations
Cited by 6 Pith papers
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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DECAF: De-Clustering for Adaptive Representational Unlearning
DECAF is a forget-only unlearning method that adds input noise, suppresses the forget-class probability, and diversifies outputs, achieving 0.10% forget accuracy and 79.4% retain accuracy on CIFAR-10/ResNet-18 while d...
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TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
TrustErase uses passport-embedded representations for instant, data-free, and auditable machine unlearning through simple deactivation of adaptation layers.
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Towards Certified Unlearning for Deep Neural Networks
Proposes simple techniques and inverse Hessian approximation to enable certified unlearning for nonconvex DNN objectives, including nonconvergent training and sequential unlearning requests.
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Machine Unlearning: A Comprehensive Survey
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.
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