  Individuals are gaining more control of their personal data through recent
data privacy laws such the General Data Protection Regulation and the
California Consumer Privacy Act. One aspect of these laws is the ability to
request a business to delete private information, the so called "right to be
forgotten" or "right to erasure". These laws have serious financial
implications for companies and organizations that train large, highly accurate
deep neural networks (DNNs) using these valuable consumer data sets. However, a
received redaction request poses complex technical challenges on how to comply
with the law while fulfilling core business operations. We introduce a DNN
model lifecycle maintenance process that establishes how to handle specific
data redaction requests and minimize the need to completely retrain the model.
Our process is based upon the membership inference attack as a compliance tool
for every point in the training set. These attack models quantify the privacy
risk of all training data points and form the basis of follow-on data redaction
from an accurate deployed model; excision is implemented through incorrect
label assignment within incremental model updates.
