Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김미란 | - |
dc.date.accessioned | 2022-10-07T04:38:58Z | - |
dc.date.available | 2022-10-07T04:38:58Z | - |
dc.date.issued | 2020-01 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v. 15, page. 695-710 | en_US |
dc.identifier.issn | 1556-6013; 1556-6021 | en_US |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8747377/ | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/175136 | - |
dc.description.abstract | Scientific collaborations benefit from sharing information and data from distributed sources, but protecting privacy is a major concern. Researchers, funders, and the public in general are getting increasingly worried about the potential leakage of private data. Advanced security methods have been developed to protect the storage and computation of sensitive data in a distributed setting. However, they do not protect against information leakage from the outcomes of data analyses. To address this aspect, studies on differential privacy (a state-of-the-art privacy protection framework) demonstrated encouraging results, but most of them do not apply to distributed scenarios. Combining security and privacy methodologies is a natural way to tackle the problem, but naive solutions may lead to poor analytical performance. In this paper, we introduce a novel strategy that combines differential privacy methods and homomorphic encryption techniques to achieve the best of both worlds. Using logistic regression (a popular model in biomedicine), we demonstrated the practicability of building secure and privacy-preserving models with high efficiency (less than 3 min) and good accuracy [<1% of difference in the area under the receiver operating characteristic curve (AUC) against the global model] using a few real-world datasets. | en_US |
dc.description.sponsorship | The work of M. Kim and X. Jiang was supported in part by the Cancer Prevention Research Institute of Texas (CPRIT) under Award RR180012 and in part by the National Institute of Health (NIH) under Award R01GM118609 and Award U01EB023685. The work of J. Lee was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) under Grant 2018R1C1B5086611. The work of L. Ohno-Machado was supported by the NIH under Award R01GM118609 and Award R01HL136835. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Logistic regression; differential privacy; homomorphic encryption | en_US |
dc.title | Secure and differentially private logistic regression for horizontally distributed data | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TIFS.2019.2925496 | en_US |
dc.relation.journal | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY | - |
dc.contributor.googleauthor | Kim, Miran | - |
dc.contributor.googleauthor | Lee, Junghye | - |
dc.contributor.googleauthor | Ohno-Machado, Lucila | - |
dc.contributor.googleauthor | Jiang, Xiaoqian | - |
dc.relation.code | 2020049099 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF NATURAL SCIENCES[S] | - |
dc.sector.department | DEPARTMENT OF MATHEMATICS | - |
dc.identifier.pid | miran | - |
dc.identifier.researcherID | AAB-2336-2020 | - |
dc.identifier.orcid | https://orcid.org/0000-0003-3564-6090 | - |
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