Smith, M.T., Alvarez, M.A., Zwiessele, M. et al. (1 more author) (2018) Differentially private regression with Gaussian processes. In: Storkey, A. and Perez-Cruz, F., (eds.) Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics 2018, 09-11 Apr 2018, Lanzarote, Canary Islands. Proceedings of Machine Learning Research (84). PMLR , pp. 1195-1203.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 The Authors. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/N014162/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Mar 2018 14:02 |
Last Modified: | 29 Jun 2020 10:03 |
Published Version: | http://proceedings.mlr.press/v84/smith18a.html |
Status: | Published |
Publisher: | PMLR |
Series Name: | Proceedings of Machine Learning Research |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128101 |