Machine Unlearning Through Fine-Grained Model Parameters Perturbation

Abstract

Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree.