Random Relabeling for Efficient Machine Unlearning
This addresses data privacy challenges for machine learning practitioners by providing a more efficient method for certified data removal, though it is incremental as it builds on existing unlearning approaches.
The paper tackles the problem of efficiently removing data from trained models to comply with privacy regulations, proposing a random relabeling scheme that handles sequential deletion requests in online settings with improved computational feasibility compared to retraining from scratch.
Learning algorithms and data are the driving forces for machine learning to bring about tremendous transformation of industrial intelligence. However, individuals' right to retract their personal data and relevant data privacy regulations pose great challenges to machine learning: how to design an efficient mechanism to support certified data removals. Removal of previously seen data known as machine unlearning is challenging as these data points were implicitly memorized in training process of learning algorithms. Retraining remaining data from scratch straightforwardly serves such deletion requests, however, this naive method is not often computationally feasible. We propose the unlearning scheme random relabeling, which is applicable to generic supervised learning algorithms, to efficiently deal with sequential data removal requests in the online setting. A less constraining removal certification method based on probability distribution similarity with naive unlearning is further developed for logit-based classifiers.