Certified Data Removal from Machine Learning Models
Abstrak
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.
Topik & Kata Kunci
Penulis (4)
Chuan Guo
T. Goldstein
Awni Y. Hannun
L. Maaten
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 617×
- Sumber Database
- Semantic Scholar
- Akses
- Open Access ✓