Towards Independence Criterion in Machine Unlearning of Features and
  Labels
Ling Han Nanqing Luo Hao Huang Jing Chen Mary-Anne Hartley
This work delves into the complexities of machine unlearning in the face of
distributional shifts, particularly focusing on the challenges posed by
non-uniform feature and label removal. With the advent of regulations like the
GDPR emphasizing data privacy and the right to be forgotten, machine learning
models face the daunting task of unlearning sensitive information without
compromising their integrity or performance. Our research introduces a novel
approach that leverages influence functions and principles of distributional
independence to address these challenges. By proposing a comprehensive
framework for machine unlearning, we aim to ensure privacy protection while
maintaining model performance and adaptability across varying distributions.
Our method not only facilitates efficient data removal but also dynamically
adjusts the model to preserve its generalization capabilities. Through
extensive experimentation, we demonstrate the efficacy of our approach in
scenarios characterized by significant distributional shifts, making
substantial contributions to the field of machine unlearning. This research
paves the way for developing more resilient and adaptable unlearning
techniques, ensuring models remain robust and accurate in the dynamic landscape
of data privacy and machine learning.