  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.
