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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.
Natural privacy filters, despite their promise for tighter privacy accounting, aren't universally "free," limiting their applicability to specific families of differentially private mechanisms.
