Homomorphic Model Selection for Data Analysis in an Encrypted Domain dagger
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Mi Yeon | - |
dc.contributor.author | Yoo, Joon Soo | - |
dc.contributor.author | Yoon, Ji Won | - |
dc.date.accessioned | 2021-08-30T15:14:24Z | - |
dc.date.available | 2021-08-30T15:14:24Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/53297 | - |
dc.description.abstract | Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Homomorphic Model Selection for Data Analysis in an Encrypted Domain dagger | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Ji Won | - |
dc.identifier.doi | 10.3390/app10186174 | - |
dc.identifier.wosid | 000580426300001 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.10, no.18 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 10 | - |
dc.citation.number | 18 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | fully homomorphic encryption | - |
dc.subject.keywordAuthor | bitwise operation | - |
dc.subject.keywordAuthor | model selection | - |
dc.subject.keywordAuthor | cross validation | - |
dc.subject.keywordAuthor | evidence approximation | - |
dc.subject.keywordAuthor | gauss-jordan elimination | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.