Certified Data Removal from Machine Learning Models

@InProceedings{pmlr-v119-guo20c,
  title = 	 {Certified Data Removal from Machine Learning Models},
  author =       {Guo, Chuan and Goldstein, Tom and Hannun, Awni and Van Der Maaten, Laurens},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {3832--3842},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/guo20c/guo20c.pdf},
  url = 	 {https://proceedings.mlr.press/v119/guo20c.html},
  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.}
}