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Unlearning can be exponentially more efficient than retraining from scratch when the unlearning requirement is sufficiently large, fundamentally changing the landscape of data removal in machine learning.
Non-private synthetic data can effectively transfer knowledge from original corpora, while state-of-the-art DP methods often fail to do so, even at high privacy levels.
