Certified Data Removal from Machine Learning Models
2019/11/08 by Chuan Guo, Guo, Chuan, Tom Goldstein +5 · 132 citations
Computer Science · #Adversarial Robustness in Machine Learning #Artificial intelligence #Certification #Computer science #Cryptography and Data Security #Machine learning #Management #Mechanism (biology) #Privacy-Preserving Technologies in Data #Stewardship (theology) #Training set
paper · pdf · doi:10.48550/arxiv.1911.03030
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.
Citations
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