Tight Bounds for Machine Unlearning via Differential Privacy

• URL: http://arxiv.org/abs/2309.00886v1
• Date: Sat, 2 Sep 2023 09:55:29 GMT
• Title: Tight Bounds for Machine Unlearning via Differential Privacy
• Authors: Yiyang Huang, Cl\'ement L. Canonne
• Abstract summary: We consider the so-called "right to be forgotten" by requiring that a trained model should be able to "unlearn" a number of points from the training data. We obtain tight bounds on the deletion capacity achievable by DP-based machine unlearning algorithms.
• Score: 0.7252027234425334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Sekhari et al. established some positive and negative results about the number of data points that can be successfully unlearnt by a trained model without impacting the model's accuracy (the "deletion capacity"), showing that machine unlearning could be achieved by using differentially private (DP) algorithms. However, their results left open a gap between upper and lower bounds on the deletion capacity of these algorithms: our work fully closes this gap, obtaining tight bounds on the deletion capacity achievable by DP-based machine unlearning algorithms.

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