Deep Regression Unlearning

With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand in a machine learning system. In past few years, there has been notable development in machine unlearning to remove the information of certain training data points efficiently and effectively from the model. In this work, we explore unlearning in a regression problem, particularly in deep learning models. Unlearning in classification and simple linear regression has been investigated considerably. However, unlearning in deep regression models largely remain an untouched problem till now. In this work, we introduce deep regression unlearning methods that are well generalized and robust to privacy attacks. We propose the Blindspot unlearning method which uses a novel weight optimization process. A randomly initialized model, partially exposed to the retain samples and a copy of original model are used together to selectively imprint knowledge about the data that we wish to keep and scrub the information of the data we wish to forget. We also propose a Gaussian distribution based fine tuning method for regression unlearning. The existing evaluation metrics for unlearning in a classification task are not directly applicable for regression unlearning. Therefore, we adapt these metrics for regression task. We devise a membership inference attack to check the privacy leaks in the unlearned regression model. We conduct the experiments on regression tasks for computer vision, natural language processing and forecasting applications. Our deep regression unlearning methods show excellent performance across all of these datasets and metrics.