Papers › Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions

Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions

15 Sep 2024arXiv:2409.09778archive 2025-07-28

Siqiao Mu, Diego Klabjan

Machine unlearning algorithms aim to efficiently remove data from a model without retraining it from scratch, in order to remove corrupted or outdated data or respect a user's ``right to be forgotten." Certified machine unlearning is a strong theoretical guarantee based on differential privacy that quantifies the extent to which an algorithm erases data from the model weights. In contrast to existing works in certified unlearning for convex or strongly convex loss functions, or nonconvex objectives with limiting assumptions, we propose the first, first-order, black-box (i.e., can be applied to models pretrained with vanilla gradient descent) algorithm for unlearning on general nonconvex loss functions, which unlearns by ``rewinding" to an earlier step during the learning process before performing gradient descent on the loss function of the retained data points. We prove (ϵ, δ) certified unlearning and performance guarantees that establish the privacy-utility-complexity tradeoff of our algorithm, and we prove generalization guarantees for nonconvex functions that satisfy the Polyak-Lojasiewicz inequality. Finally, we implement our algorithm under a new experimental framework that more accurately reflects real-world use cases for preserving user privacy.

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2ran · honoured contract
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compute_test_error siqiaomu/r2d/r2d.py found in paper text by Syntology ran · honoured contract MIT (permissive) · 6c94779e31eb4e12 · report
get_error siqiaomu/r2d/r2d.py found in paper text by Syntology ran · our draft was wrong fingerprinted MIT (permissive) · de35b5dbce9d27e9 · report
h_function siqiaomu/r2d/r2d.py found in paper text by Syntology ran · honoured contract fingerprinted MIT (permissive) · 8b0a5db3b9d9ea07 · report
newton_update zhangbinchi/certified-deep-unlearning/unlearn.py found in paper text by Syntology unverified MIT (permissive) · 1acec0665cb7fb8e · report
hvp identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · e261328dc1f89c4a · report

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Machine Unlearning

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