  When developing models for regulated decision making, sensitive features like
age, race and gender cannot be used and must be obscured from model developers
to prevent bias. However, the remaining features still need to be tested for
correlation with sensitive features, which can only be done with the knowledge
of those features. We resolve this dilemma using a fully homomorphic encryption
scheme, allowing model developers to train linear regression and logistic
regression models and test them for possible bias without ever revealing the
sensitive features in the clear. We demonstrate how it can be applied to
leave-one-out regression testing, and show using the adult income data set that
our method is practical to run.
