  The Right to be Forgotten is part of the recently enacted General Data
Protection Regulation (GDPR) law that affects any data holder that has data on
European Union residents. It gives EU residents the ability to request deletion
of their personal data, including training records used to train machine
learning models. Unfortunately, Deep Neural Network models are vulnerable to
information leaking attacks such as model inversion attacks which extract class
information from a trained model and membership inference attacks which
determine the presence of an example in a model's training data. If a malicious
party can mount an attack and learn private information that was meant to be
removed, then it implies that the model owner has not properly protected their
user's rights and their models may not be compliant with the GDPR law. In this
paper, we present two efficient methods that address this question of how a
model owner or data holder may delete personal data from models in such a way
that they may not be vulnerable to model inversion and membership inference
attacks while maintaining model efficacy. We start by presenting a real-world
threat model that shows that simply removing training data is insufficient to
protect users. We follow that up with two data removal methods, namely
Unlearning and Amnesiac Unlearning, that enable model owners to protect
themselves against such attacks while being compliant with regulations. We
provide extensive empirical analysis that show that these methods are indeed
efficient, safe to apply, effectively remove learned information about
sensitive data from trained models while maintaining model efficacy.
