Machine Unlearning via Algorithmic Stability

• URL: http://arxiv.org/abs/2102.13179v1
• Date: Thu, 25 Feb 2021 21:16:56 GMT
• Title: Machine Unlearning via Algorithmic Stability
• Authors: Enayat Ullah, Tung Mai, Anup Rao, Ryan Rossi, Raman Arora
• Abstract summary: We study the problem of machine unlearning and identify a notion of Total Variation (TV) stability. For convex risk minimization problems, we design TV-stable algorithms based on noisy Gradient Descent (SGD) Our techniques to generalize our algorithms are differentially private as well.
• Score: 31.809738694273623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearning. For convex risk minimization problems, we design TV-stable algorithms based on noisy Stochastic Gradient Descent (SGD). Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a (maximal) coupling of Markov chains for the noisy SGD procedure. To understand the trade-offs between accuracy and unlearning efficiency, we give upper and lower bounds on excess empirical and populations risk of TV stable algorithms for convex risk minimization. Our techniques generalize to arbitrary non-convex functions, and our algorithms are differentially private as well.

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