Paper 2016/111

Scalable and Secure Logistic Regression via Homomorphic Encryption

Yoshinori Aono, Takuya Hayashi, Le Trieu Phong, and Lihua Wang

Abstract

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for 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 instance. Our system is very scalable in both the dataset size and dimension, tolerating big size for example of hundreds of millions ($10^8$s) records. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its accuracies and other related measures such as F-score and AUC.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Published elsewhere. Major revision. IEICE Transactions 99-D(8): 2079-2089 (2016)
DOI
10.1587/transinf.2015INP0020
Keywords
logistic regressionhomomorphic encryptionPaillierLWEring-LWEoutsourced computationaccuracyF-scorearea under curve
Contact author(s)
phong @ nict go jp
History
2017-03-31: revised
2016-02-10: received
See all versions
Short URL
https://ia.cr/2016/111
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2016/111,
      author = {Yoshinori Aono and Takuya Hayashi and Le Trieu Phong and Lihua Wang},
      title = {Scalable and Secure Logistic Regression via Homomorphic Encryption},
      howpublished = {Cryptology ePrint Archive, Paper 2016/111},
      year = {2016},
      doi = {10.1587/transinf.2015INP0020},
      note = {\url{https://eprint.iacr.org/2016/111}},
      url = {https://eprint.iacr.org/2016/111}
}
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