HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning

Software systems that learn from user data with machine learning (ML) have become ubiquitous over the last years. Recent law requires organisations that process personal data to delete user data upon request (enacting the right to be forgotten). However, it is not sufficient to merely delete the user data from databases. ML models that have been learned from the stored data often resemble a lossy compressed version of the data, and pose a potential privacy risk. We therefore argue that ML applications should also provide "privacy through deletion" by offering users to "unlearn" their data from trained models. In order to account for the constraints imposed by real world deployments, we 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 reaccessing its training data. We propose "HedgeCut", a classification model based on an ensemble of randomized decision trees, which is designed to answer unlearning requests with low latency. We detail how to efficiently implement HedgeCut with vectorised operators for decision tree learning. We conduct an experimental evaluation on five privacy sensitive datasets, where we find that HedgeCut can unlearn training samples with a latency of around 100 microseconds and answers up to 36,000 prediction requests per second, while providing a training time and predictive accuracy similar to widely used implementations of tree-based ML models such as Random Forests.