Differentially Private Gaussian Processes

Abstract

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.

9 pages + 4 supplementary material pages, 6 plots grouped into 5 figures, accepted at AISTATS 2018

References in corpus (2)

• Differentially Private Bayesian Learning on Distributed Data
• Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

Cited by in corpus (5)

• Data Science vs. Statistics: Two Cultures?
• Differentially Private Regression and Classification with Sparse Gaussian Processes
• No-Regret Algorithms for Private Gaussian Process Bandit Optimization
• Scalable Cross Validation Losses for Gaussian Process Models
• Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption