Learn to Forget: Machine Unlearning via Neuron Masking
Zhuo Ma, Yang Liu, Ximeng Liu, Jian Liu, Jianfeng Ma, Kui Ren
Abstract
Nowadays, machine learning models, especially neural networks, have became prevalent in many real-world applications. These models are trained based on a one-way trip from user data: as long as users contribute their data, there is no way to withdraw. To this end, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine unlearning</i> becomes a popular research topic, which allows the model trainer to unlearn unexpected data from a trained machine learning model. In this article, we propose the first uniform metric called forgetting rate to measure the effectiveness of a machine unlearning method. It is based on the concept of membership inference and describes the transformation rate of the eliminated data from “memorized” to “unknown” after conducting unlearning. We also propose a novel unlearning method called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Forsaken</monospace> . It is superior to previous work in either utility or efficiency (when achieving the same forgetting rate). We benchmark <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Forsaken</monospace> with eight standard datasets to evaluate its performance. The experimental results show that it can achieve more than 90% forgetting rate on average and only causeless than 5% accuracy loss.