Differentially Private Gaussian Processes
This work addresses privacy concerns in data analysis for applications like healthcare or finance, though it is incremental as it builds on existing differential privacy and Gaussian process frameworks.
The paper tackles the challenge of enabling machine learning on sensitive data by combining differential privacy with Gaussian processes for regression, achieving improved accuracy through a noise-covariance cloaking method that minimizes added noise while ensuring privacy guarantees.
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.