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.
@article{arxiv.1911.03030,
title = {Certified Data Removal from Machine Learning Models},
author = {Chuan Guo and Tom Goldstein and Awni Hannun and Laurens van der Maaten},
journal= {arXiv preprint arXiv:1911.03030},
year = {2023}
}