Journal article
Encrypted accelerated least squares regression
- Abstract:
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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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(Version of record, pdf, 405.9KB)
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Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Grant:
EP/F500394/1, EP/K014463/1
Bibliographic Details
- 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:
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1938-7228
- Source identifiers:
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689000
Item Description
- Keywords:
- Pubs id:
-
pubs:689000
- UUID:
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uuid:65e979d7-8734-4719-a033-9d39b35dfc17
- Local pid:
- pubs:689000
- Deposit date:
- 2017-04-11
Terms of use
- Copyright holder:
- Esperança et al
- Copyright date:
- 2017
- Notes:
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Proceedings of the 20th International Conference on Artifi-
cial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA. JMLR: W&CP volume 54. Copyright 2017 by the author(s). Freely available online at [http://proceedings.mlr.press/v54/esperanca17a.html].
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