Minimax Gaussian Mechanisms for Continual Machine Unlearning
The research develops Gaussian mechanisms for machine unlearning that approximate retraining without full cost, adhering to Gaussian Differential Privacy constraints. It compares Independent Noise and Gaussian Random Walk methods, optimizing noise allocation to minimize error variance across sequential deletions. The findings provide rigorous bounds ensuring consistency in parameter and prediction errors.