  We introduce a general learning framework for private machine learning based
on randomised response. Our assumption is that all actors are potentially
adversarial and as such we trust only to release a single noisy version of an
individual's datapoint. We discuss a general approach that forms a consistent
way to estimate the true underlying machine learning model and demonstrate this
in the case of logistic regression.
