IEICE TRANSACTIONS on Information
Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption
Summary :
Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.8 pp.2079-2089
- Publication Date
- 2016/08/01
- Publicized
- 2016/05/31
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015INP0020
- Type of Manuscript
- Special Section PAPER (Special Section on Security, Privacy and Anonymity of Internet of Things)
- Category
Authors
Yoshinori AONO
National Institute of Information and Communications Technology (NICT)
Takuya HAYASHI
National Institute of Information and Communications Technology (NICT)
Le Trieu PHONG
National Institute of Information and Communications Technology (NICT)
Lihua WANG
National Institute of Information and Communications Technology (NICT)
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Yoshinori AONO, Takuya HAYASHI, Le Trieu PHONG, Lihua WANG, "Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 8, pp. 2079-2089, August 2016, doi: 10.1587/transinf.2015INP0020.
Abstract: Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015INP0020/_p
Copy
@ARTICLE{e99-d_8_2079,
author={Yoshinori AONO, Takuya HAYASHI, Le Trieu PHONG, Lihua WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption},
year={2016},
volume={E99-D},
number={8},
pages={2079-2089},
abstract={Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.},
keywords={},
doi={10.1587/transinf.2015INP0020},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption
T2 - IEICE TRANSACTIONS on Information
SP - 2079
EP - 2089
AU - Yoshinori AONO
AU - Takuya HAYASHI
AU - Le Trieu PHONG
AU - Lihua WANG
PY - 2016
DO - 10.1587/transinf.2015INP0020
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2016
AB - Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.
ER -
English
