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arXiv · 1911.03030

Certified Data Removal from Machine Learning Models

Chuan Guo·Tom Goldstein·Awni Hannun·Laurens van der Maaten
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Abstract

Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.

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Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten. 2019-11-08. Certified Data Removal from Machine Learning Models. https://arxiv.org/abs/1911.03030

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