Differentially Private Gaussian Processes

Michael Thomas Smith; Max Zwiessele; Neil D. Lawrence

edit

Back to publications

Differentially Private Gaussian Processes

Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence

, 2016.

Abstract

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Differential privacy is a framework which allows algorithms to have provable privacy guarantees. Gaussian processes are a widely used approach for dealing with uncertainty in functions. This paper explores differentially private mechanisms for Gaussian processes. We compare binning and adding noise before regression with adding noise post-regression. For the former we develop a new kernel for use with binned data. For the latter we show that using inducing inputs allows us to reduce the scale of the added perturbation. We find that, for the datasets used, adding noise to a binned dataset has superior accuracy. Together these methods provide a starter toolkit for combining differential privacy and Gaussian processes.

Links

Cite this Paper

BibTeX


@Misc{Smith-dpgp16,
  title = 	 {Differentially Private {G}aussian Processes},
  author = 	 {Smith, Michael Thomas and Zwiessele, Max and Lawrence, Neil D.},
  year = 	 {2016},
  pdf = 	 {https://arxiv.org/abs/1606.00720},
  url = 	 {/publications/smith-dpgp16.html},
  abstract = 	 {A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Differential privacy is a framework which allows algorithms to have provable privacy guarantees. Gaussian processes are a widely used approach for dealing with uncertainty in functions. This paper explores differentially private mechanisms for Gaussian processes. We compare binning and adding noise before regression with adding noise post-regression. For the former we develop a new kernel for use with binned data. For the latter we show that using inducing inputs allows us to reduce the scale of the added perturbation. We find that, for the datasets used, adding noise to a binned dataset has superior accuracy. Together these methods provide a starter toolkit for combining differential privacy and Gaussian processes.}
}

Endnote

%0 Generic
%T Differentially Private Gaussian Processes
%A Michael Thomas Smith
%A Max Zwiessele
%A Neil D. Lawrence
%D 2016	
%F Smith-dpgp16
%U /publications/smith-dpgp16.html
%X A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Differential privacy is a framework which allows algorithms to have provable privacy guarantees. Gaussian processes are a widely used approach for dealing with uncertainty in functions. This paper explores differentially private mechanisms for Gaussian processes. We compare binning and adding noise before regression with adding noise post-regression. For the former we develop a new kernel for use with binned data. For the latter we show that using inducing inputs allows us to reduce the scale of the added perturbation. We find that, for the datasets used, adding noise to a binned dataset has superior accuracy. Together these methods provide a starter toolkit for combining differential privacy and Gaussian processes.

RIS


TY  - GEN
TI  - Differentially Private Gaussian Processes
AU  - Michael Thomas Smith
AU  - Max Zwiessele
AU  - Neil D. Lawrence
DA  - 2016/06/02	
ID  - Smith-dpgp16
L1  - https://arxiv.org/abs/1606.00720
UR  - /publications/smith-dpgp16.html
AB  - A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Differential privacy is a framework which allows algorithms to have provable privacy guarantees. Gaussian processes are a widely used approach for dealing with uncertainty in functions. This paper explores differentially private mechanisms for Gaussian processes. We compare binning and adding noise before regression with adding noise post-regression. For the former we develop a new kernel for use with binned data. For the latter we show that using inducing inputs allows us to reduce the scale of the added perturbation. We find that, for the datasets used, adding noise to a binned dataset has superior accuracy. Together these methods provide a starter toolkit for combining differential privacy and Gaussian processes.
ER  -

APA


Smith, M.T., Zwiessele, M. & Lawrence, N.D.. (2016). Differentially Private Gaussian Processes.  Available from /publications/smith-dpgp16.html.