Learn to Forget: Machine Unlearning via Neuron Masking
This addresses the legal and privacy issue of data memorization in machine learning models for users and organizations under regulations like GDPR, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of enabling neural networks to forget specific training data to comply with GDPR's right to be forgotten, proposing a method that achieves over 90% forgetting rate with less than 5% accuracy loss on average across eight datasets.
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.