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Unlearning algorithms aim to remove deleted data's influence from trainedmodels at a cost lower than full retraining. However, prior guarantees ofunlearning in literature are flawed and don't protect the privacy of deletedrecords. We show that when users delete their data as a function of publishedmodels, records in a database become interdependent. So, even retraining afresh model after deletion of a record doesn't ensure its privacy. Secondly,unlearning algorithms that cache partial computations to speed up theprocessing can leak deleted information over a series of releases, violatingthe privacy of deleted records in the long run. To address these, we propose asound deletion guarantee and show that the privacy of existing records isnecessary for the privacy of deleted records. Under this notion, we propose anaccurate, computationally efficient, and secure machine unlearning algorithmbased on noisy gradient descent.

Forget Unlearning: Towards True Data-Deletion in Machine Learning