# Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning Source: [Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning]() Collection index: [llms.txt]() Data updated: 2026-06-02T05:00:12.522Z Authors: Polina Dolgova, Sebastian U. Stich Published: 2026-01-08T17:23:13.000Z ## Abstract Certified unlearning based on differential privacy offers strong guarantees but remains largely impractical: the noisy fine-tuning approaches proposed so far achieve these guarantees but severely reduce model accuracy. We propose sequential noise scheduling, which distributes the noise budget across orthogonal subspaces of the parameter space, rather than injecting it all at once. This simple modification mitigates the destructive effect of noise while preserving the original certification guarantees. We extend the analysis of noisy fine-tuning to the subspace setting, proving that the same $(\\varepsilon,\\delta)$ privacy budget is retained. Empirical results on image classification benchmarks show that our approach substantially improves accuracy after unlearning while remaining robust to membership inference attacks. These results show that certified unlearning can achieve both rigorous guarantees and practical utility. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()