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Logistic regression model training based on the approximate homomorphic encryption

DC Field Value Language
dc.contributor.authorKim, Andrey-
dc.contributor.authorSong, Yongsoo-
dc.contributor.authorKim, Miran-
dc.contributor.authorLee, Keewoo-
dc.contributor.authorCheon, Jung Hee-
dc.date.accessioned2019-01-17T05:23:34Z-
dc.date.available2019-01-17T14:24:37Z-
dc.date.issued2018-10-11-
dc.identifier.citationBMC Medical Genomics, 11(Suppl 4):83ko_KR
dc.identifier.issn1755-8794-
dc.identifier.urihttps://hdl.handle.net/10371/145164-
dc.description.abstractBackground
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 Nesterovs 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.
ko_KR
dc.description.sponsorshipThis work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.B0717-16-0098) and by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (No.2017R1A5A1015626).
MK was supported in part by NIH grants U01TR002062 and U01EB023685. Publication of this article has been funded by the NRF Grant funded by the Korean Government (MSIT) (No.2017R1A5A1015626).
ko_KR
dc.language.isoenko_KR
dc.subjectHomomorphic encryptionko_KR
dc.subjectMachine learningko_KR
dc.subjectLogistic regressionko_KR
dc.titleLogistic regression model training based on the approximate homomorphic encryptionko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor송용수-
dc.contributor.AlternativeAuthor이기우-
dc.contributor.AlternativeAuthor전정희-
dc.identifier.doi10.1186/s12920-018-0401-7-
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2018-10-14T03:23:03Z-
Appears in Collections:
College of Natural Sciences (자연과학대학)Dept. of Mathematical Sciences (수리과학부)Journal Papers (저널논문_수리과학부)
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