Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer
  Level Attack and Knowledge Distillation
Hyunjune Kim Sangyong Lee Simon S. Woo
Recently, serious concerns have been raised about the privacy issues related
to training datasets in machine learning algorithms when including personal
data. Various regulations in different countries, including the GDPR grant
individuals to have personal data erased, known as 'the right to be forgotten'
or 'the right to erasure'. However, there has been less research on effectively
and practically deleting the requested personal data from the training set
while not jeopardizing the overall machine learning performance. In this work,
we propose a fast and novel machine unlearning paradigm at the layer level
called layer attack unlearning, which is highly accurate and fast compared to
existing machine unlearning algorithms. We introduce the Partial-PGD algorithm
to locate the samples to forget efficiently. In addition, we only use the last
layer of the model inspired by the Forward-Forward algorithm for unlearning
process. Lastly, we use Knowledge Distillation (KD) to reliably learn the
decision boundaries from the teacher using soft label information to improve
accuracy performance. We conducted extensive experiments with SOTA machine
unlearning models and demonstrated the effectiveness of our approach for
accuracy and end-to-end unlearning performance.