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Encrypted accelerated least squares regression

Abstract:

Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed analysis of coordinate and accelerated gradient descent algorithms which are capable of fitting least squares and penalised ridge regression models, using data encrypted under a fully homomorphic encryption scheme. Gradient descent is shown to dominate in terms of encrypted computational speed, and theoretical results ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
Wadham College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author

Contributors

Role:
Editor
Role:
Editor
Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
AISTATS 2017: The 20th International Conference on Artificial Intelligence and Statistics Journal website
Volume:
54
Pages:
334-343
Series:
Proceedings of Machine Learning Research
Publication date:
2017-04-10
Acceptance date:
2017-02-05
ISSN:
1938-7228
Source identifiers:
689000
Keywords:
Pubs id:
pubs:689000
UUID:
uuid:65e979d7-8734-4719-a033-9d39b35dfc17
Local pid:
pubs:689000
Deposit date:
2017-04-11

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