Differentially private regression with gaussian processes

Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish
Number of pages9
Publication statusPublished - 2018
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: 9 Apr 201811 Apr 2018


Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
CityPlaya Blanca, Lanzarote, Canary Islands


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