  Recently enacted legislation grants individuals certain rights to decide in
what fashion their personal data may be used, and in particular a "right to be
forgotten". This poses a challenge to machine learning: how to proceed when an
individual retracts permission to use data which has been part of the training
process of a model? From this question emerges the field of machine unlearning,
which could be broadly described as the investigation of how to "delete
training data from models". Our work complements this direction of research for
the specific setting of class-wide deletion requests for classification models
(e.g. deep neural networks). As a first step, we propose linear filtration as a
intuitive, computationally efficient sanitization method. Our experiments
demonstrate benefits in an adversarial setting over naive deletion schemes.
