  Nowadays, machine learning models, especially neural networks, become
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; and it is well-known that a neural network memorizes its
training data. This contradicts the "right to be forgotten" clause of GDPR,
potentially leading to law violations. To this end, machine unlearning becomes
a popular research topic, which allows users to eliminate memorization of their
private data from a trained machine learning model.In this paper, we propose
the first uniform metric called for-getting 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 calledForsaken. It is superior to previous work in either
utility or efficiency (when achieving the same forgetting rate). We benchmark
Forsaken 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.
