Privacy preserving machine unlearning for smart cities

Abstract

Due to emerging concerns about public and private privacy issues in smart cities, many countries and organizations are establishing laws and regulations (e.g., GPDR) to protect the data security. One of the most important items is the so-called The Right to be Forgotten, which means that these data should be forgotten by all inappropriate use. To truly forget these data, they should be deleted from all databases that cover them, and also be removed from all machine learning models that are trained on them. The second one is called machine unlearning. One naive method for machine unlearning is to retrain a new model after data removal. However, in the current big data era, this will take a very long time. In this paper, we borrow the idea of Generative Adversarial Network (GAN), and propose a fast machine unlearning method that unlearns data in an adversarial way. Experimental results show that our method produces significant improvement in terms of the forgotten performance, model accuracy, and time cost.