Minimax Gaussian Mechanisms for Continual Machine Unlearning
Read the original on arXiv Machine Learning →The paper introduces Gaussian mechanisms for Newton updates that enable continual machine unlearning—updating a model after record deletions without full retraining. Using Gaussian differential privacy and adaptive composition, it shows that the sequence of released models is statistically indistinguishable from exact retraining. The authors derive error bounds for the Newton approximation, calibrate independent and random‑walk Gaussian noise, and demonstrate minimax optimality under various residual and increment bounds, with empirical validation on simulations and credit‑default data.
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