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Tight Bounds for Machine Unlearning via Differential Privacy
We consider the formulation of "machine unlearning" of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called "right to be forgotten" by requiring that a trained model, upon request, should be able to "unlearn" a number of points from the training data, as if they had never been included in the first place.
Invertible Mask Network for Face Privacy-Preserving
Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face.
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