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Logistic regression model training based on the approximate homomorphic encryption

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Authors
Kim, Andrey; Song, Yongsoo; Kim, Miran; Lee, Keewoo; Cheon, Jung Hee
Issue Date
2018-10-11
Citation
BMC Medical Genomics, 11(Suppl 4):83
Keywords
Homomorphic encryptionMachine learningLogistic regression
Abstract
Background
Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives.

Methods
This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier.

Results
Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable.

Conclusions
We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.
ISSN
1755-8794
Language
English
URI
http://hdl.handle.net/10371/145164
DOI
https://doi.org/10.1186/s12920-018-0401-7
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College of Natural Sciences (자연과학대학)Dept. of Mathematical Sciences (수리과학부)Journal Papers (저널논문_수리과학부)
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