Selective Unlearning via Representation Erasure Using Domain Adversarial Training
Nazanin Mohammadi Sepahvand Eleni Triantafillou Hugo Larochelle Doina Precup James J. Clark Daniel M. Roy Gintare Karolina Dziugaite
When deploying machine learning models in the real world,  we often face the challenge of “unlearning” specific data points or subsets after training.  Inspired by Domain-Adversarial Training of Neural Networks (DANN), we propose a novel algorithm,SURE, for targeted unlearning.SURE treats the process as a domain adaptation problem, where the “forget set” (data to be removed) and a validation set from the same distribution form two distinct domains. We train a domain classifier to discriminate between representations from the forget and validation sets.Using a gradient reversal strategy similar to DANN, we perform gradient updates to the representations to “fool” the domain classifier and thus obfuscate representations belonging to the forget set. Simultaneously, gradient descent is applied to the retain set (original training data minus the forget set) to preserve its classification performance.  Unlike other unlearning approaches whose training objectives are built based on model outputs, SURE directly manipulates the representations.This is key to ensure robustness against a set of more powerful attacks than currently considered in the literature,  that aim to detect which examples were unlearned through access to learned embeddings.  Our thorough experiments reveal that SURE has a better unlearning quality to utility trade-off compared to other standard unlearning techniques for deep neural networks.
approximate unlearning, adversarial training