  A major challenge for machine learning is increasing the availability of data
while respecting the privacy of individuals. Here we combine the provable
privacy guarantees of the differential privacy framework with the flexibility
of Gaussian processes (GPs). We propose a method using GPs to provide
differentially private (DP) regression. We then improve this method by crafting
the DP noise covariance structure to efficiently protect the training data,
while minimising the scale of the added noise. We find that this cloaking
method achieves the greatest accuracy, while still providing privacy
guarantees, and offers practical DP for regression over multi-dimensional
inputs. Together these methods provide a starter toolkit for combining
differential privacy and GPs.
