HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning
Sebastian Schelter, Stefan Grafberger, Ted Dunning
Abstract
Software systems that learn from user data with machine learning (ML) have become ubiquitous over the last years. Recent law such as the "General Data Protection Regulation" (GDPR) requires organisations that process personal data to delete user data upon request (enacting the "right to be forgotten"). However, this regulation does not only require the deletion of user data from databases, but also applies to ML models that have been learned from the stored data. We therefore argue that ML applications should offer users to unlearn their data from trained models in a timely manner. We explore how fast this unlearning can be done under the constraints imposed by real world deployments, and introduce the problem of low-latency machine unlearning: maintaining a deployed ML model in-place under the removal of a small fraction of training samples without retraining.